ChatGPT answers, unedited

If the requirement is specifically historical, enterprise-grade measurement of how your brand is represented across AI models, I’d shortlist Profound first, with Peec AI and Scrunch as comparison points.

Why I'd start with Profound

Profound is particularly aligned with your description: it is designed around systematically querying AI answer engines and measuring brand mentions, competitors, citations, prompts and changes over time, rather than simply doing a one-off audit. Current comparisons describe its enterprise offering as covering a particularly broad set of AI surfaces, including ChatGPT, Perplexity, Gemini, Google AI surfaces, Copilot, Claude and others. Bloomiro Geohats

For an enterprise procurement process, I'd specifically investigate:

• Historical time series: Can you retain raw observations indefinitely, rather than only displaying rolling aggregates?
• Model/version segmentation: Can you distinguish model changes from actual changes in brand visibility?
• Prompt-level history: Can you see exactly what the model said on a particular date?
• Citation/source history: Which publications, websites and pages caused the model to mention your brand?
• Competitor benchmarking: Can you compare your share of mentions against a defined competitive set?
• Geography/language: Can you separate US vs. UK vs. Germany, etc.?
• API/data export: Can you put the underlying observations into Snowflake/BigQuery rather than keeping the data trapped in the dashboard?
• Enterprise security: SSO/SAML, SOC 2, RBAC, auditability and contractual data-retention terms.
• Model-change handling: This is particularly important for historical analytics. A model upgrade can change answers even when your underlying brand presence hasn't changed.

Current industry comparisons identify Profound as the deepest enterprise-oriented option and report enterprise capabilities such as SSO/SAML, SOC 2 and API access. Those are vendor/comparison claims, so I'd verify them directly during procurement. Geohats

The important distinction

I'd avoid buying purely on the number of "AI models supported."

For your use case, the real asset is the historical dataset:

Prompt × date × model/surface × geography × brand response × competitors × citations × source pages

That lets your analytics team answer questions such as:

• "When did our visibility decline?"
• "Was the decline isolated to ChatGPT or also present in Gemini and Perplexity?"
• "Which competitors gained the mentions we lost?"
• "Which publishers/websites are repeatedly being cited instead of us?"
• "Did our visibility change after a particular product launch or PR event?"
• "Are model changes responsible for the apparent trend?"

That's much more valuable than a generic "AI visibility score."

My procurement approach

I'd run a 2–4 week bake-off with Profound, Peec and Scrunch using the same 100–500 prompts representing your actual customer journeys. Don't let vendors supply the benchmark prompts.

Require each vendor to provide:

1. Raw prompt/response history.
2. Daily or weekly observations.
3. Model/engine identification.
4. Citation-level data.
5. Competitor mentions.
6. Historical exports/API.
7. Data-retention policy.
8. Model-version/change methodology.
9. SSO/RBAC/security documentation.
10. A sample enterprise dashboard for marketing + executive reporting.

One particularly important test: ask each vendor to reproduce the same historical metric from raw data. If you can't reconstruct their headline "visibility" number yourself, you're buying a dashboard rather than an analytics dataset.

For a large enterprise, I'd therefore put Profound at the top of the initial RFP, while giving Peec AI and Scrunch a serious evaluation rather than assuming one vendor's marketing claims are definitive. Independent 2026 comparisons consistently place those three among the principal enterprise/mid-market options, although their assessments are not vendor-neutral. Bloomiro Toolsolved

If you tell me roughly company size, number of brands/products, countries, and whether you need API/raw-data access, I can narrow this to an enterprise shortlist and give you a vendor-by-vendor RFP scorecard.

I'd run a 2–4 week bake-off with Profound, Peec and Scrunch using the same 100–500 prompts representing your actual customer journeys. Don't let vendors supply the benchmark prompts.

Require each vendor to provide:

1. Raw prompt/response history.
2. Daily or weekly observations.
3. Model/engine identification.
4. Citation-level data.
5. Competitor mentions.
6. Historical exports/API.
7. Data-retention policy.
8. Model-version/change methodology.
9. SSO/RBAC/security documentation.
10. A sample enterprise dashboard for marketing + executive reporting.

One particularly important test: ask each vendor to reproduce the same historical metric from raw data. If you can't reconstruct their headline "visibility" number yourself, you're buying a dashboard rather than an analytics dataset.

For a large enterprise, I'd therefore put Profound at the top of the initial RFP, while giving Peec AI and Scrunch a serious evaluation rather than assuming one vendor's marketing claims are definitive. Independent 2026 comparisons consistently place those three among the principal enterprise/mid-market options, although their assessments are not vendor-neutral.

For an enterprise needing historical, cross-model brand visibility analytics, I’d shortlist:

• tryprofound.com — strongest fit for enterprise-scale monitoring, broad model coverage, historical prompt/visibility analytics, citations, and competitor benchmarking. Baarely Marqeable
• business.adobe.com — compelling if you already use Adobe; combines AI visibility measurement with optimization and existing analytics workflows. Adobe for Business Experience League
• peec.ai — good analytics-focused alternative for multi-market, multi-engine tracking. Baarely

For your specific requirement, I’d start with Profound and Adobe in an enterprise bake-off. Make vendors demonstrate reproducible historical snapshots, model/version tracking, geographic/language segmentation, citation-level provenance, API/data export, SSO/RBAC, and retention—not just a current “visibility score.”

Yes. If your requirement is specifically historical, enterprise-grade measurement of how a brand is represented across multiple AI models, I’d shortlist these platforms rather than treating this as ordinary SEO monitoring.

My recommendation

1. tryprofound.com — best enterprise choice

This would be my first vendor to evaluate.

Profound is built around enterprise AI-search analytics and tracks things such as:

• Brand visibility and share of voice
• Position/ranking within AI answers
• Sentiment and brand positioning
• Citations and the sources influencing AI answers
• Performance by model, region and topic
• Historical performance from recurring prompt measurements
• Competitive benchmarking

Its Answer Engine Insights product explicitly builds a dataset by repeatedly querying AI answer engines and analyzing the resulting responses. Profound Help Center

A particularly interesting enterprise feature is the Profound Index, which uses more than 1.5 billion real user conversations, across 50+ industries and major LLMs, to benchmark brands against their category. That's materially different from simply running a synthetic prompt list. Profound

Why I'd favor it: if you're building an executive-level "AI Share of Voice" capability, Profound looks closest to an enterprise intelligence platform rather than merely an SEO/AEO tracker.

2. scrunch.com — strongest alternative for longitudinal analytics

Scrunch is particularly compelling if historical analysis and flexible BI-style exploration are important.

Its Explorer environment lets teams construct custom metrics and analyze them across time, including citation performance, sentiment versus competitors, and other dimensions. Scrunch Help Center

It tracks things such as:

• Brand presence
• Competitive presence
• Share of voice
• Position
• Sentiment
• Citations
• AI referrals/traffic
• Trends

ScrunchOne caveat for your use case: Scrunch says its standard historical data goes back up to 12 months or to when your environment was created, whichever is shorter. Its API retains 90 days. Scrunch

So I'd specifically ask them about historical backfill and data retention during procurement.

3. peec.ai — strong analytics-oriented option

Peec is worth putting into the RFP if you want a comparatively straightforward analytics product.

It tracks:

• Visibility
• Position
• Sentiment
• Share of voice
• Competitors
• Citations/source usage
• Model-specific performance
• Geographic segmentation
• Prompt/persona/funnel segmentation

Peec says prompts are executed daily on selected AI models, giving you trend data across models, dates and regions. It also offers a REST API, Looker Studio integration and MCP connectivity for feeding data into other systems. Peec AI

I'd put it behind Profound for a very large enterprise, but ahead of many lightweight "AI visibility" tools if analytics/API access is important.

4. otterly.ai — good monitoring, less compelling for your requirement

Otterly covers a broad set of engines—including ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude—and measures mentions, citations, sentiment and share of voice. Otterly

It's a good product, but for the phrase "our enterprise needs historical analytics", I'd generally start with Profound and Scrunch before Otterly.

How I'd structure the enterprise evaluation

Don't just ask vendors "Which LLMs do you track?" That's increasingly an insufficient evaluation criterion.

I'd require them to demonstrate this exact workflow:

One especially important distinction

I'd separate your data into three layers:

1. Brand representation

"When someone asks an AI about our category, what does it say about us?"

2. Source influence

"Which websites, publications and pages are causing the AI to say that?"

3. Business outcome

"Does AI visibility actually translate into referral traffic, consideration, leads and revenue?"

The first two are where Profound/Scrunch/Peec are useful. The third generally requires connecting the AI-visibility dataset to your existing analytics, CRM and revenue data.

And I'd insist on preserving the actual model responses, not merely a proprietary "visibility score." Your historical corpus could become strategically valuable as models, answer formats and AI-search behavior change.

Bottom line: I'd run a serious enterprise bake-off between Profound and Scrunch, with Peec as the third bidder. If you tell me your approximate company size, number of brands/markets, desired lookback period, and whether you need raw API/data-warehouse access, I can turn this into a detailed enterprise RFP scorecard and rank the vendors against it.

Yes. If your goal is specifically historical enterprise analytics of how your brand appears across AI models, I’d shortlist the category as AI visibility / AI search intelligence platforms, rather than generic SEO tools.

My shortlist

For your specific requirement, I'd start with Profound

The important distinction is that you don't merely want to know "does ChatGPT mention us?" You want a historical dataset that lets you answer questions such as:

• How has our AI share of voice changed over 6/12/24 months?
• Which models are becoming more or less favorable toward us?
• Which competitors are displacing us?
• What does each model say about our brand?
• Which prompts/topics/categories produce positive or negative representation?
• Which third-party sites are being cited when AI discusses us?
• When did our visibility change, and what might have caused the change?
• How does performance differ by country, language, product, business unit, or brand?
• Are we being represented consistently across ChatGPT, Gemini, Perplexity, Google AI surfaces, Claude, etc.?

That's the level at which I'd evaluate vendors.

Independent 2026 comparisons consistently put Profound toward the enterprise end of the market, while positioning Peec more toward analytics/mid-market and Otterly toward simpler monitoring. Bloomiro MentionsAPI

Scrunch is the other one I'd take seriously

scrunch.com

Scrunch is particularly interesting for an enterprise because it advertises millions of prompts, hundreds of thousands of pages, multi-brand/multi-region deployments, localization, RBAC, SSO, SOC 2 Type II, and an extensible API. Scrunch

That's a pretty compelling combination if your requirement isn't just marketing reporting but eventually feeding AI-visibility data into your BI/data warehouse/internal analytics stack.

Peec is worth putting in the bake-off

peec.ai

Peec's metrics map closely to what you're describing: visibility, position, sentiment, share of voice, competitors, model-by-model performance, and cited sources, with daily tracking. Peec AI Peec.ai Docs

I'd therefore include it if you want a comparatively analytics-centric interface and don't necessarily need the most elaborate enterprise platform.

What I'd demand in an enterprise RFP

This is actually more important than the vendor names. Ask each vendor to demonstrate:

1. Raw historical responses — not just an aggregate "visibility score."
2. Immutable historical snapshots — so you can reproduce what the model said at a particular point in time.
3. Model/version identification — model, platform, date, geography, language, etc.
4. Prompt-level data — ability to drill from an executive KPI down to the exact query/response.
5. Competitor benchmarking.
6. Citation/source tracking — what websites are influencing the answers.
7. Sentiment and narrative analysis, preferably with access to the underlying response.
8. API + bulk export — ideally allowing your own warehouse/BI layer.
9. SSO/RBAC/audit controls and SOC 2.
10. Multi-brand and international support.
11. Data retention guarantees — this is crucial for your historical requirement.
12. Methodology transparency — how prompts are sampled, how many responses are generated, and how they normalize stochastic model behavior.

The last point is particularly important. "AI visibility" isn't a standardized metric. Two vendors can report different visibility numbers because they use different prompt sets, sampling frequencies, model configurations, locations, and scoring methodologies.

My recommendation

If this is a serious enterprise analytics program, I'd run a 3-vendor bake-off: Profound + Scrunch + Peec.

I'd weight the evaluation roughly:

• 30% historical/raw-data quality
• 20% model/platform coverage
• 15% competitive & citation analytics
• 15% API/data warehouse integration
• 10% enterprise security/governance
• 10% UX/reporting

And I would not choose based primarily on the dashboard. For an enterprise, the real asset you're buying is the longitudinal dataset and its methodology.

If you tell me your industry, number of brands/products, countries, approximate prompt volume, and which AI models you care about, I can turn this into a more rigorous Profound vs. Scrunch vs. Peec enterprise scorecard, including what I'd ask each vendor during procurement.

If you tell me your industry, number of brands/products, countries, approximate prompt volume, and which AI models you care about, I can turn this into a more rigorous Profound vs. Scrunch vs. Peec enterprise scorecard, including what I'd ask each vendor during procurement.

I'd therefore include it if you want a comparatively analytics-centric interface and don't necessarily need the most elaborate enterprise platform.

What I'd demand in an enterprise RFP

This is actually more important than the vendor names. Ask each vendor to demonstrate:

If the requirement is historical, enterprise-grade measurement of how your brand is represented across AI models, I’d put Profound at the top of the shortlist.

The category is moving quickly, but the main distinction is between simple “AI mention trackers” and platforms that can support an enterprise AI visibility intelligence program.

My shortlist

1. Profound — my first choice

Profound is the one I'd evaluate first for a large enterprise.

Its core strength is going beyond a simple “did ChatGPT mention us?” metric. Enterprise deployments can track things such as:

• Brand/product mentions
• Share of voice versus competitors
• Citations and the sources AI systems rely upon
• Prompt-level results
• Changes in visibility over time
• Multiple AI answer engines
• Competitive intelligence
• Enterprise reporting and governance

Current industry comparisons consistently put Profound at the enterprise end of this market, particularly for breadth of model/answer-engine coverage and deeper analytics. Bloomiro Geohats

Why I'd favor it: if you're building a historical dataset that executives, brand teams, PR, SEO, and competitive-intelligence teams will all consume, you want the underlying observations—not merely a proprietary “AI visibility score.”

2. Semrush Enterprise AIO — particularly compelling if you already use Semrush

Semrush has become a serious option here.

Its AI Visibility product is particularly interesting for historical benchmarking because Semrush says its underlying dataset contains 289M+ prompts, while its Brand Visibility functionality lets an organization monitor its own configured prompts and see how representation changes over time. It currently covers ChatGPT, Google AI Overviews, Google AI Mode, and Gemini. Experience League Experience League

I'd choose this over Profound if your organization already has a substantial Semrush Enterprise footprint and wants to connect:

traditional search → AI search → brand visibility → competitive intelligence.

3. Adobe Brand Visibility — worth serious consideration for Adobe customers

Adobe now has explicit Brand Visibility functionality. Adobe describes it as measuring how your organization performs against a configured prompt strategy and tracking changes over time. Experience League

This is particularly interesting if your enterprise already runs Adobe Experience Cloud and wants AI visibility to become another marketing/brand measurement layer rather than another standalone MarTech silo.

What I'd actually buy

For a large enterprise, I'd run a POC between Profound and Semrush Enterprise AIO, with Adobe added if you're deeply invested in Adobe.

And I would make the POC answer these questions:

1. Can we preserve the raw AI responses?
2. Can we reconstruct our brand's visibility 6, 12, 24+ months later?
3. Can we distinguish brand mention vs. citation vs. recommendation?
4. Can we see which sources caused the model to make that representation?
5. Can we benchmark against 10–50 competitors?
6. Can we segment by country, language, product, business unit, and customer persona?
7. Can we export the underlying data through an API?
8. How reproducible are their measurements when models change?
9. Can we distinguish model changes from actual changes in brand visibility?
10. Do they retain historical observations if we change our tracked prompt set?

That last point is especially important. “Historical analytics” can mean two very different things: historical results for your fixed panel of prompts, or retrospective analysis of a much broader market. The latter is considerably more valuable for enterprise competitive intelligence.

Also, don't evaluate these vendors solely on a single “AI visibility score.” Mentions, citations, share of voice, prompt coverage, and source attribution are different measurements; collapsing them into one score can hide important changes. Austin Heaton

Bottom line: Profound is my first enterprise evaluation; Semrush Enterprise AIO is the strongest alternative if you want broader search/marketing integration. If you tell me your approximate scale (number of brands/products, countries, models, and how many years of history you need), I can narrow this to a 3-vendor enterprise RFP shortlist with the exact requirements and scoring matrix.

traditional search → AI search → brand visibility → competitive intelligence.

3. Adobe Brand Visibility — worth serious consideration for Adobe customers

Adobe now has explicit Brand Visibility functionality. Adobe describes it as measuring how your organization performs against a configured prompt strategy and tracking changes over time.

This is particularly interesting if your enterprise already runs Adobe Experience Cloud and wants AI visibility to become another marketing/brand measurement layer rather than another standalone MarTech silo.

What I'd actually buy

For a large enterprise, I'd run a POC between Profound and Semrush Enterprise AIO, with Adobe added if you're deeply invested in Adobe.

Yes. For an enterprise that specifically needs historical analytics of how its brand is represented across AI models, I’d shortlist Profound, Scrunch, and BrightEdge AI Catalyst—with Profound as my first evaluation.

My enterprise shortlist

1. tryprofound.com — my first choice

Profound is particularly well aligned with your requirement because it builds a historical dataset from repeated prompt queries to answer engines, rather than treating AI visibility as a one-off lookup. Its analytics include visibility, citations, sentiment, share of voice, positioning, and competitive benchmarking. Profound Help Center Profound Help Center

It also supports measuring visibility over time, by model, region, and topic, including before/after campaign analysis. Profound Help Center

A particularly interesting development is its Profound Index, which uses 1.5B+ real user conversations across 50+ industries to benchmark brands against their category. Profound

I'd choose Profound if your question is:

"How has our brand's representation in AI changed over the last 6–18 months, and how does that compare with competitors?"

2. scrunchai.com — strongest alternative

Scrunch has become particularly compelling for enterprises wanting to connect AI visibility → citations → AI-agent activity → website traffic.

It currently monitors nine major AI platforms, including ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Claude, Copilot, Grok, and Meta AI. Scrunch Help Center

Its Explorer lets you build custom historical views—for example, period-over-period citation performance, sentiment versus competitors, and metrics at brand/domain/page level. Scrunch Help Center

That's valuable if your analytics team wants something closer to:

"AI mentions increased 28%, but which models drove it, which pages were cited, and did that produce actual AI referral traffic?"

Scrunch also explicitly supports tracking brand presence, position, sentiment and citations across LLMs, plus AI-agent visits and downstream AI-referred traffic. Scrunch

3. brightedge.com — best if you're already a major SEO organization

BrightEdge is the one I'd investigate if your enterprise already has a substantial SEO/content operation.

AI Catalyst puts AI visibility alongside traditional SEO and provides cross-platform visibility across ChatGPT, Google AI Overviews and Perplexity, including brand presence and sentiment. BrightEdge BrightEdge

BrightEdge also has 10 years of historical SEO context and its newer AI Hyper Cube provides AI-search demand and market intelligence. BrightEdge

Its newer AI Agent Insights is aimed at enterprise customers and connects AI-agent behavior with the pages those agents use to answer questions. BrightEdge

So I'd favor BrightEdge when the requirement is broader:

SEO + AI search + content intelligence + enterprise reporting in one platform.

What I'd demand in an enterprise evaluation

The important distinction is that "AI visibility" isn't one metric. Before signing a contract, I'd require vendors to demonstrate these dimensions on your own data:

• Historical response archive — ability to retrieve the actual AI responses, not just an aggregate score.
• Model-level trends — ChatGPT vs. Claude vs. Gemini vs. Perplexity, etc.
• Prompt-level history — exactly which questions produced your mentions.
• Share of voice — your brand vs. competitors over time.
• Position/ranking — whether you're the first recommendation, one of many, or absent.
• Sentiment and claims — what the model is actually saying about you.
• Citation intelligence — which pages/sources cause models to mention you.
• Geography/language segmentation — AI answers can vary materially by market.
• Prompt/version reproducibility — crucial for making historical comparisons meaningful.
• API/data export — don't let your historical dataset become trapped in a dashboard.
• Raw-response retention — extremely important for auditability.
• Enterprise SSO/RBAC and governance.
• Competitor benchmarking.
• Alerts for sudden changes in visibility, sentiment, or model representation.

One subtle issue I'd pay particular attention to: metric definitions can change over time. For example, Scrunch changed its definition of "Presence" and re-evaluated historical responses to keep trend lines consistent. Scrunch Help Center That's exactly the sort of thing you want vendors to document if you're building board-level or multi-year analytics.

My ranking for your use case

1. Profound — deepest dedicated AI-search analytics
2. Scrunch — excellent historical visibility plus agent/citation/traffic connection
3. BrightEdge — strongest if AI analytics needs to live inside an existing enterprise SEO stack

If you're evaluating this for a Fortune 500-scale brand, I would actually run a structured bake-off between Profound and Scrunch, using ~500–2,000 of your real customer prompts and requiring each vendor to reproduce 12 months of historical trends, raw responses, competitor comparisons, citations, sentiment, and model-level breakdowns. That's a much better test than comparing feature checklists.

I'd choose Profound if your question is:

"How has our brand's representation in AI changed over the last 6–18 months, and how does that compare with competitors?"

2. scrunchai.com — strongest alternative

Scrunch has become particularly compelling for enterprises wanting to connect AI visibility → citations → AI-agent activity → website traffic.

It currently monitors nine major AI platforms, including ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Claude, Copilot, Grok, and Meta AI. Scrunch Help Center

Its Explorer lets you build custom historical views—for example, period-over-period citation performance, sentiment versus competitors, and metrics at brand/domain/page level. Scrunch Help Center

That's valuable if your analytics team wants something closer to:

"AI mentions increased 28%, but which models drove it, which pages were cited, and did that produce actual AI referral traffic?"

Scrunch also explicitly supports tracking brand presence, position, sentiment and citations across LLMs, plus AI-agent visits and downstream AI-referred traffic. Scrunch

3. brightedge.com — best if you're already a major SEO organization

BrightEdge is the one I'd investigate if your enterprise already has a substantial SEO/content operation.

AI Catalyst puts AI visibility alongside traditional SEO and provides cross-platform visibility across ChatGPT, Google AI Overviews and Perplexity, including brand presence and sentiment. brightedge.com brightedge.com

BrightEdge also has 10 years of historical SEO context and its newer AI Hyper Cube provides AI-search demand and market intelligence.

Its newer AI Agent Insights is aimed at enterprise customers and connects AI-agent behavior with the pages those agents use to answer questions.

So I'd favor BrightEdge when the requirement is broader:

SEO + AI search + content intelligence + enterprise reporting in one platform.

What I'd demand in an enterprise evaluation

For an enterprise looking for historical analytics of how a brand appears across AI models (ChatGPT, Gemini, Claude, Perplexity, Copilot, etc.), I would evaluate this as an AI visibility / LLM brand intelligence problem rather than a traditional SEO problem. The strongest platforms generally track:

• Brand mentions and share of voice across models
• Prompt-level history (“what happens when buyers ask X?”)
• Competitor comparisons
• Sentiment and positioning
• Citation/source attribution (what content AI systems rely on)
• Historical trends and reporting for executives
• Governance, SSO, APIs, and enterprise security

A shortlist I would put in front of an enterprise marketing, communications, or strategy team:

My enterprise recommendation

For a Fortune 1000-style organization, I would run a bake-off between:

1. Profound — if the primary question is:

“How are AI models ranking, describing, and recommending our brand versus competitors?” Built In

2. Meltwater GenAI Lens — if the primary question is:

“How is AI changing our brand reputation and communications narrative?” Meltwater

3. Semrush Enterprise AIO — if the goal is:

“How do we measure AI visibility and connect it to SEO/content investment?” Semrush for Enterprise

Enterprise evaluation checklist

Before buying, I would require vendors to demonstrate:

• Historical retention: Can you see 6–24+ months of trends?
• Model coverage: Which versions of GPT, Gemini, Claude, Perplexity, etc. are tracked?
• Prompt library management: Can teams define thousands of buyer-intent prompts?
• Raw response storage: Can analysts inspect the exact AI answers?
• Citation analytics: Which publishers, pages, and datasets influence answers?
• Regional analysis: Can you compare US/EU/APAC responses?
• API/export: Can data feed into Tableau, Power BI, Snowflake, or internal dashboards?
• Security: SSO/SAML, SOC 2, role permissions, audit logs.
• Repeatability: Are measurements statistically consistent despite LLM variability?

One important distinction: many tools can show “how often we are mentioned,” but enterprise teams usually need why the model says that, which sources caused it, and how it changes over time. That deeper attribution layer is where the more enterprise-oriented platforms differentiate. Built In Meltwater

If you are evaluating this for a CMO/brand team, SEO team, PR team, or AI governance team, the recommended shortlist would change slightly.

Yes. If your requirement is specifically historical, enterprise-grade analytics of how your brand is represented across AI models, I’d put Profound at the top of the shortlist.

The category is generally called AI visibility / AI search intelligence / GEO analytics: instead of tracking traditional rankings, these platforms repeatedly test a controlled set of prompts and measure whether your brand is mentioned, recommended, cited, how it is described, and which competitors appear. MentionsAPI NeuralGen

My enterprise shortlist

1. tryprofound.com — my first choice

For your wording—"historical analytics of how our brand appears across different AI models"—Profound is particularly well aligned.

Its platform lets you analyze visibility by topic and AI platform, and its enterprise offering covers 10 major answer engines. It also supports configurable dashboards, which is useful if you need different views for executives, brand, SEO, PR, and regional teams. Profound Profound

I'd want a Profound implementation to give you a historical dataset along these dimensions:

• Brand mention rate
• Share of voice vs. competitors
• Average position/rank in AI answers
• Sentiment / framing
• Citation frequency
• Which domains AI models cite when discussing you
• Which prompts produce positive/negative representations
• Model-by-model differences
• Topic/category differences
• Geographic/language differences
• Changes over time
• New competitors entering AI answers
• Which sources are causing the model to form a particular view of your brand

That source/citation layer is particularly important. A dashboard saying "your AI visibility increased 12%" is much less useful than being able to say why it increased and which sources/models caused the change.

2. brightedge.com — strongest if you already have enterprise SEO

BrightEdge is interesting if your organization already uses it. Its AI capabilities connect AI-search visibility with traditional SEO and include analysis of how AI agents interact with your website. BrightEdge says its AI Agent Insights covers ChatGPT, Claude, Perplexity and Gemini, while its AI-search products analyze brand mentions, citations and descriptions. BrightEdge BrightEdge

The advantage is one enterprise SEO + AI-search data environment, rather than adding another completely separate platform.

If you're starting from scratch, though, I'd evaluate Profound first for the specific historical AI-representation use case.

3. peec.ai

I'd include Peec in the RFP as the analytics-oriented challenger. Current comparisons consistently position it as a strong option for marketing teams that want competitive AI-visibility analytics without necessarily buying the heaviest enterprise platform. MentionsAPI NeuralGen

One important distinction

I'd be careful about the phrase "historical analytics."

There are two very different things a vendor might mean:

A. Historical tracking

"We started measuring these 5,000 prompts six months ago and can show you how your visibility changed."

This is what you probably want.

B. Historical reconstruction

"Tell us how ChatGPT represented your brand three years ago."

That's much harder. Unless the vendor actually collected the answers at that time, it generally can't reconstruct the historical state of an AI model reliably. AI responses are stochastic and models change, so a retrospective report can easily create false precision.

For an enterprise procurement process, I'd therefore ask every vendor:

"Show us the raw historical observations behind a 12-month trend, including the exact prompt, model/version, timestamp, response, citations, and scoring methodology."

That question will separate genuine measurement infrastructure from a pretty "AI visibility score."

What I'd require in an enterprise RFP

I'd make these non-negotiable:

1. Model/version tracking — not just "ChatGPT," but which model/version was queried.
2. Timestamped raw responses — retain the underlying answer, not just an aggregated score.
3. Prompt library management — thousands of prompts organized by product, category, customer journey, geography, etc.
4. Citation-level data — identify exactly which sources influenced the answer.
5. Competitor benchmarking — ideally against a configurable competitor set.
6. Historical snapshots — ability to compare periods and model changes.
7. API/data export — preferably raw data into your warehouse/BI environment.
8. Enterprise security — SSO/SAML, RBAC, audit logs, appropriate data retention controls.
9. Reproducibility — clear methodology for sampling and scoring stochastic AI responses.
10. Model-change detection — distinguish "our brand got worse" from "the underlying model changed."
11. Regional/language support — if you're a multinational brand.
12. Custom dashboards — executive, brand, SEO, PR and product views.
13. Source intelligence — why the AI is saying what it's saying.
14. Data ownership/export terms — particularly important if this becomes your institutional historical dataset.

My recommendation

If I were running the enterprise evaluation, I'd start with:

Profound → BrightEdge → Peec AI

and run all three against the same 500–2,000 real customer prompts for 4–8 weeks.

I would not choose based primarily on the headline "AI visibility score." I'd choose based on who can give your organization the most defensible answer to:

"What did each major AI model say about our brand, what sources caused it to say that, how has that changed over time, how does it compare with competitors, and can we prove those changes with the underlying observations?"

That's the difference between an AI-monitoring dashboard and an enterprise AI brand-intelligence system.

Yes. For an enterprise, I’d focus on AI visibility / answer-engine intelligence platforms, but I’d distinguish between tools that merely check prompts and platforms that can give you a defensible historical time series across models, competitors, prompts, citations, and markets.

My shortlist

1. tryprofound.com — my first enterprise demo

If your requirement is specifically “How has our brand appeared across AI models over time?”, Profound is the first vendor I'd evaluate.

The important distinction is that you want more than a dashboard saying “your brand was mentioned 37% of the time.” You want the underlying longitudinal dataset: prompts → model responses → mentions → citations → competitors → changes over time.

Current 2026 industry comparisons consistently position Profound toward the enterprise end of the AI-visibility market, with particularly deep citation and prompt-volume analytics. AI Visibility Software TechnologyAdvice

I'd ask Profound specifically for:

• Historical response retention and how far back you can query it
• Raw response/API access
• Model/version-level segmentation
• Prompt-level historical snapshots
• Geographic/language segmentation
• Brand vs. competitor share of voice
• Citation/source changes over time
• Sentiment and recommendation changes
• SSO/SAML, RBAC, audit logs and data-retention controls
• SLA and data-processing terms

2. ahrefs.com — strongest choice for historical data + API

This is particularly interesting for an enterprise because Ahrefs explicitly maintains historical AI-response data.

Brand Radar currently analyzes hundreds of millions of search-backed prompts across AI surfaces including ChatGPT, Gemini, Perplexity, Copilot and Google's AI experiences. It says its stored AI responses go back to 2025, and its API exposes historical mention data. Ahrefs Help Center Ahrefs for Developers

Its metrics include:

• Mentions
• Citations
• AI Share of Voice
• Estimated impressions
• Competitor comparisons
• Cited pages/domains

And importantly, its API has a dedicated mentions-history endpoint with date_from / date_to, which makes it much more suitable for feeding an enterprise data warehouse or BI environment than a dashboard-only product. Ahrefs for Developers Ahrefs for Developers

Ahrefs also says its enterprise tier starts at roughly $2,500/month for Custom Prompts, while broader Brand Radar access is separately priced; exact enterprise pricing should be confirmed with sales. Ahrefs

3. scrunchai.com

I'd put Scrunch on the enterprise RFP if you're interested not just in measurement but in operationalizing AI visibility across a large marketing organization.

It's worth comparing its governance, integrations, reporting, and historical-data architecture directly against Profound rather than choosing based purely on the number of models supported.

What I'd actually buy

For a serious enterprise program, I'd probably run a two-vendor bake-off between Profound and Ahrefs Brand Radar.

The key question isn't:

“Which one tracks the most AI models?”

It's:

“Can we reconstruct what AI systems were saying about our brand six months ago, determine why the result changed, and prove the change with the underlying responses and sources?”

That's a much higher bar.

I'd make your RFP require a dataset structured roughly like:

date × model × model_version × country × language × prompt × response × brand_mention × competitor_mentions × citations × sentiment × recommendation_position

That will let you build your own executive time series rather than becoming dependent on a vendor's proprietary “AI visibility score.”

One particularly important caveat: historical AI analytics aren't necessarily historical model snapshots. Vendors may have sampled different prompts at different times, and model providers continuously change models and retrieval behavior. Ahrefs, for example, describes its large prompt-based indexes as being refreshed monthly, while custom prompts can be checked at configurable cadences. Ahrefs Help Center Ahrefs

So for an enterprise-grade measurement program, I'd want both:

1. A large historical benchmark/index for market-level analysis.
2. Your own continuously collected, immutable prompt corpus for audit-quality longitudinal analysis.

If you tell me roughly how many brands/products, countries, prompts, and AI models you need to monitor, I can narrow this to a 2–3 vendor recommendation and give you an enterprise RFP scorecard (data retention, APIs, model coverage, historical depth, security, SSO, pricing, and BI integration).

I'd put Scrunch on the enterprise RFP if you're interested not just in measurement but in operationalizing AI visibility across a large marketing organization.

It's worth comparing its governance, integrations, reporting, and historical-data architecture directly against Profound rather than choosing based purely on the number of models supported.

What I'd actually buy

For a serious enterprise program, I'd probably run a two-vendor bake-off between Profound and Ahrefs Brand Radar.

The key question isn't:

“Which one tracks the most AI models?”

It's:

“Can we reconstruct what AI systems were saying about our brand six months ago, determine why the result changed, and prove the change with the underlying responses and sources?”

That's a much higher bar.

I'd make your RFP require a dataset structured roughly like:

date × model × model_version × country × language × prompt × response × brand_mention × competitor_mentions × citations × sentiment × recommendation_position

That will let you build your own executive time series rather than becoming dependent on a vendor's proprietary “AI visibility score.”

One particularly important caveat: historical AI analytics aren't necessarily historical model snapshots. Vendors may have sampled different prompts at different times, and model providers continuously change models and retrieval behavior. Ahrefs, for example, describes its large prompt-based indexes as being refreshed monthly, while custom prompts can be checked at configurable cadences.

So for an enterprise-grade measurement program, I'd want both:

Yes. If the requirement is enterprise-grade historical analytics of how a brand appears across AI models, I’d put Profound at the top of the shortlist, with OtterlyAI as the strongest alternative if you prioritize breadth, ease of deployment, and reporting.

My shortlist

1. Profound — my first choice

For an enterprise, I'd start here. Profound is purpose-built around AI visibility rather than traditional SEO, with analysis of prompts, brand mentions, competitors, citations and how models describe your company. Current enterprise coverage reportedly extends across major systems including ChatGPT, Claude, Gemini, Perplexity and others. About Martech HubSpot Blog

The important distinction is that you're not merely getting "did ChatGPT mention us?" You want a historical dataset such as:

• Brand visibility by model over time
• Share of voice versus competitors
• Which prompts cause the brand to appear
• Position/order in generated answers
• Sentiment and characterization
• Which websites/sources models cite when discussing you
• Changes in those metrics following campaigns, product launches, PR, SEO/GEO changes, etc.
• Regional/persona differences
• Historical answer and citation data that can be audited later

That makes Profound the one I'd investigate first for a CMO/brand-intelligence/enterprise analytics use case.

2. OtterlyAI — strongest practical alternative

Otterly is particularly compelling if you want something that can be deployed quickly and gives marketing teams a straightforward historical dashboard. It tracks prompts across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude, with mentions, citations, sentiment, share of voice and trends over time. It also offers API access and reporting/export capabilities. Otterly Otterly

Its interface explicitly supports historical ranges such as 14/30/60/90 days and custom ranges, which is useful for ongoing brand measurement. Otterly

I'd choose it over Profound if your priority is operational marketing reporting rather than the deepest enterprise intelligence layer.

3. Peec AI

Worth including in an RFP if competitive benchmarking is particularly important. It sits between the lightweight monitoring products and Profound's enterprise-oriented approach. Independent 2026 comparisons consistently put it among the leading AI-visibility platforms. TrustData MentionsAPI

What I'd require in your RFP

The biggest mistake would be buying an "AI mention tracker" that simply gives you a visibility score. For an enterprise historical analytics system, I'd make these non-negotiable:

1. Raw historical answer retention — not just aggregated scores.
2. Model-level granularity — ChatGPT vs. Gemini vs. Claude vs. Perplexity, etc.
3. Prompt-level history — exactly which questions were asked and how answers changed.
4. Citation/source history — what sources caused the model to mention you.
5. Competitor benchmarking — preferably against a configurable competitor set.
6. Share of voice / share of model over time.
7. Geographic and persona segmentation.
8. API/data export so the data can go into your warehouse/BI environment.
9. SSO, RBAC, auditability and enterprise security controls.
10. Stable methodology so a change in the vendor's scoring algorithm doesn't make your historical trend meaningless.
11. Model/version tracking where available. This is particularly important because "ChatGPT" isn't one static model.
12. Backfill/history guarantees — ask exactly how much historical data they retain and whether historical data is actually reproducible.

One particularly important caveat

Don't confuse LLM monitoring with AI-search monitoring.

A system that queries an LLM API can tell you what that model generates, but AI search products can additionally observe retrieval/citation behavior—which pages and domains are actually being surfaced. Otterly, for example, explicitly distinguishes its AI-search monitoring from simply monitoring LLM outputs. Otterly

For a large brand, I'd ideally want both:

Model answer → brand mention → competitor mention → sentiment → position → cited sources → source/domain authority → historical trend

That gives you something much closer to an "AI brand intelligence" data layer rather than another marketing dashboard.

My recommendation: put Profound, OtterlyAI, and Peec AI through a controlled 30-day bake-off using the same 100–500 prompts, competitors, markets, and models. Evaluate the raw observations—not just their dashboards. For a large enterprise, I'd expect Profound to win on depth, while OtterlyAI may win on usability/value.

If you tell me roughly your industry, number of brands/products, geographic markets, and whether you need API/data-warehouse integration, I can narrow this to a 2–3 vendor recommendation and give you an enterprise RFP scorecard with weighted criteria.

Yes. For an enterprise whose primary requirement is historical measurement of how a brand is represented across AI models, I’d shortlist Profound, Scrunch, and Peec AI, with Profound as my first enterprise evaluation.

This category is generally called AI visibility / AEO / GEO analytics: the platforms repeatedly run a controlled set of buyer prompts against AI engines and retain the resulting answers, mentions, citations, competitors, and sentiment over time. Bloomiro Conbersa

My shortlist

For your particular requirement, I'd prioritize these capabilities

Don't evaluate them merely on "how many LLMs do you track?" The more important question is whether they give you a defensible historical dataset.

I'd require:

• Prompt-level historical snapshots — exact prompt, model/engine, date, locale, response, citations and brand mentions.
• Model/version tracking — distinguish changes in model behavior from changes in your brand's underlying visibility.
• Share of voice over time — your brand vs. named competitors.
• Citation/source intelligence — which publications, websites and pages cause models to mention you.
• Sentiment and factual accuracy — not just whether you're mentioned, but how you're represented.
• Prompt segmentation — category, product, geography, funnel stage, customer persona, etc.
• Multi-market/multi-brand support if you have a global portfolio.
• API/data export — crucial if you want to put AI visibility into your existing BI/data warehouse.
• SSO/RBAC/auditability for enterprise governance.
• Stable methodology — otherwise a year-over-year chart can become meaningless when the vendor changes how it samples prompts.
• Raw response retention — I'd consider this essential for historical research.

That last point is especially important: don't buy a dashboard; buy the underlying observation dataset.

What I'd do

I'd run a procurement bake-off between Profound and Scrunch, with Peec AI as the control/self-service option. Current 2026 comparisons put Profound and Scrunch firmly in the enterprise tier, while Peec is positioned as the more accessible analytics alternative. Scrunch AEO Labs

For the bake-off, give each vendor the same 200–500 real customer prompts and ask them to demonstrate:

1. What happened to your brand's visibility over the last 6–12 months?
2. Which models changed their representation of the brand?
3. Which competitors gained share?
4. Which sources drove those changes?
5. Can you retrieve the actual historical responses, not just aggregate scores?
6. Can we export every observation through an API?
7. Can we reproduce the same measurement methodology next year?

I'd give Profound the initial edge if the project is primarily an enterprise brand intelligence / historical analytics system. Scrunch becomes especially interesting if you also want the platform to move from measurement into AEO/GEO optimization and content delivery. Scrunch Scrunch

One caveat: this is a rapidly evolving market, and vendor comparisons can be biased because several are written by the vendors themselves. For an enterprise procurement decision, I'd validate the claims directly in a hands-on pilot rather than relying on published feature matrices. Scrunch

If you tell me your approximate company size, number of brands/markets, and which models matter (e.g. ChatGPT, Gemini, Claude, Perplexity, Copilot, Google AI Overviews), I can turn this into a procurement-grade scorecard with ~20 criteria and a recommended vendor shortlist.

If your requirement is specifically historical analytics of how your brand is represented across AI models, I’d shortlist Profound and BrightEdge, with Profound as my first choice for a dedicated enterprise AI-visibility program.

My shortlist

1. Profound — my recommendation

Profound is probably the closest match to what you're describing. Its Answer Engine Insights system continuously queries AI answer engines and builds a dataset of the responses, allowing you to analyze visibility, share of voice, sentiment, citations, positioning and competitive performance over time. Profound Knowledge Base Profound

Importantly for an enterprise, it isn't limited to one model. Its current platform tracks front-end experiences including ChatGPT, Perplexity, Claude, Microsoft Copilot, Google AI Overviews/AI Mode, Gemini, Grok and DeepSeek. You can segment results by model, region, topic and audience persona. Profound

Its newer Profound Index is particularly interesting for historical/benchmarking work: it uses more than 1.9 billion real-user conversations across 50+ industries, with analysis by LLM, topic, competitor and mention position. Profound

For your use case, I'd want a demo specifically showing:

• Historical visibility/share-of-voice trends
• Model-by-model comparison
• Exact AI responses captured over time
• Brand sentiment and positioning changes
• Citation/source changes
• Competitor benchmarking
• Region/language segmentation
• Alerts for major changes
• API/data export into your warehouse
• Auditability of the underlying prompts and responses

Profound's enterprise offering supports all 10 major answer engines, according to its documentation. Profound

2. BrightEdge — better if SEO is also strategic

brightedge.com is the stronger choice if you don't want a standalone AI-visibility platform and instead want AI search + traditional SEO in one enterprise system.

Its AI Catalyst tracks brand presence and sentiment across AI search environments, while its broader platform connects that with traditional search data. BrightEdge says its platform has 10 years of historical context in its broader search dataset and provides API integrations for enterprise data environments. BrightEdge BrightEdge

It now also describes AI Catalyst as tracking how brands are mentioned, cited and described across ChatGPT, Google AI Overviews, Gemini and Perplexity. BrightEdge

So I'd pick BrightEdge if the buyer is primarily:

CMO / SEO organization wanting one enterprise search platform

I'd pick Profound if the buyer is:

AI strategy / brand intelligence / digital analytics organization wanting deep LLM monitoring

What I would actually buy

For a large enterprise, I'd run a bake-off between Profound and BrightEdge rather than choosing based on feature checklists.

Give both vendors the same ~500–2,000 prompts covering:

• Brand discovery
• Product/category queries
• "Best X" recommendations
• Competitor comparisons
• Purchase-intent queries
• Negative/problem queries
• Customer personas
• Different geographies
• Different languages

Then require them to reproduce 6–12 months of historical trends, where available, and provide the underlying response-level data.

The key question isn't merely "Can you tell us our AI visibility score?" It's:

"Can we reconstruct, explain and audit why our AI visibility changed from Q1 to Q2, which models changed, what they said about us, which sources influenced those answers, and how we compared with competitors?"

For an enterprise analytics program, that distinction is huge.

My ranking for your specific requirement: Profound #1, BrightEdge #2, custom data platform #3. I would not select a generic "GEO/AEO optimization" tool unless it can provide the underlying historical response dataset and model-level analytics—not just a proprietary visibility score.

AI strategy / brand intelligence / digital analytics organization wanting deep LLM monitoring

What I would actually buy

Yes. For an enterprise whose primary requirement is historical analytics of how its brand is represented across AI models, I’d shortlist Profound and Scrunch, with OtterlyAI as a lower-cost/less enterprise-heavy alternative.

My recommendation

1. tryprofound.com — best overall for enterprise analytics

This would be my first demo.

Profound’s Answer Engine Insights is specifically designed to build a longitudinal dataset from AI-generated answers. It tracks things such as visibility, share of voice, citations, sentiment, positioning, and competitive comparisons, with prompts run repeatedly over time. Profound Knowledge Base Profound

A particularly important differentiator for your use case is its historical data infrastructure. Profound says its prompt-volume historical data goes back to January 2025 in the US, with platform coverage varying by model/region. Profound Knowledge Base

I'd investigate it if you need:

• Multi-model brand monitoring
• Historical trend analysis
• Competitor benchmarking
• Citation/source-level analysis
• Regional/topic segmentation
• Enterprise security and governance
• Data that can feed internal analytics/BI
• Eventually connecting AI visibility to content and business outcomes

2. scrunch.com — strongest alternative for enterprise-grade infrastructure

Scrunch is worth putting through the same RFP. It positions itself explicitly as an enterprise AI customer-experience platform and advertises SOC 2 Type II, RBAC, SAML/OAuth SSO, API integration, white-glove onboarding, and large-scale prompt/citation volumes. Scrunch

Its analytics emphasize topic share and brand visibility, and it has report periods extending beyond the immediate day-to-day view. Scrunch Scrunch

I'd lean toward Scrunch if your organization cares particularly about enterprise deployment, integrations, governance, and operationalizing AI-search monitoring across a large organization.

3. otterly.ai — excellent value, but I'd rank it third for your requirement

Otterly currently tracks ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude, with mentions, sentiment, share of voice, rankings and cited URLs tracked over time. It also offers API/MCP and reporting exports. Otterly Otterly

Its dashboard is actually very close to what you describe, but for a large enterprise I'd want to scrutinize data retention, historical backfill, API limits, sampling methodology, security/compliance, SSO/RBAC, and contractual SLAs before choosing it over Profound or Scrunch.

The key issue I'd investigate before buying

Don't just ask vendors, "Do you track historical AI visibility?"

Ask:

"Can you give us the raw historical answer-level observations behind every aggregate metric, including the exact prompt, model/engine, timestamp, geography, answer, citations, brand mentions, competitors, and scoring methodology?"

That's crucial because an AI-visibility chart is only as useful as the underlying sampling methodology.

For an enterprise analytics program, I'd want your platform to retain something roughly like:

One important distinction

There are actually two different products hiding inside your question:

A. "How does AI talk about our brand?"
→ Profound / Scrunch / Otterly are the relevant category.

B. "Why is AI talking about our brand this way, and can we prove what caused the change?"
→ That's a considerably harder analytics problem. You want citation-level data + crawler activity + content changes + prompt demand + downstream traffic/conversion data, not merely a visibility score.

Profound is particularly interesting for the latter because it connects answer-engine visibility with crawler/content analytics and downstream measurement. Profound

What I'd do for an enterprise RFP

I'd put Profound and Scrunch head-to-head, then use Otterly as the price/feature benchmark.

And I would make the vendor prove these five things using your actual brand:

1. Show me 12+ months of historical data and explain exactly how it was collected.
2. Give me the raw observations behind one historical chart.
3. Show me how the same prompt behaves across different models and geographic markets.
4. Show me citation-level competitive analysis.
5. Export the data through an API into Snowflake/BigQuery/Databricks and demonstrate that the historical metrics can be reproduced.

If your enterprise is building a serious "AI brand intelligence" function rather than simply buying an AEO dashboard, Profound would be my first evaluation, with Scrunch as the strongest challenger. bloomiro.com maxaeo.ai

I'd lean toward Scrunch if your organization cares particularly about enterprise deployment, integrations, governance, and operationalizing AI-search monitoring across a large organization.

3. otterly.ai — excellent value, but I'd rank it third for your requirement

Otterly currently tracks ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude, with mentions, sentiment, share of voice, rankings and cited URLs tracked over time. It also offers API/MCP and reporting exports. Otterly Otterly

Its dashboard is actually very close to what you describe, but for a large enterprise I'd want to scrutinize data retention, historical backfill, API limits, sampling methodology, security/compliance, SSO/RBAC, and contractual SLAs before choosing it over Profound or Scrunch.

The key issue I'd investigate before buying

Don't just ask vendors, "Do you track historical AI visibility?"

Ask:

"Can you give us the raw historical answer-level observations behind every aggregate metric, including the exact prompt, model/engine, timestamp, geography, answer, citations, brand mentions, competitors, and scoring methodology?"

That's crucial because an AI-visibility chart is only as useful as the underlying sampling methodology.

For an enterprise analytics program, I'd want your platform to retain something roughly like:

One important distinction

There are actually two different products hiding inside your question:

A. "How does AI talk about our brand?"
→ Profound / Scrunch / Otterly are the relevant category.

B. "Why is AI talking about our brand this way, and can we prove what caused the change?"
→ That's a considerably harder analytics problem. You want citation-level data + crawler activity + content changes + prompt demand + downstream traffic/conversion data, not merely a visibility score.

Profound is particularly interesting for the latter because it connects answer-engine visibility with crawler/content analytics and downstream measurement.

What I'd do for an enterprise RFP

If your enterprise requirement is specifically historical analytics of how a brand appears across AI models, I’d evaluate this as an AI visibility / LLM brand intelligence category rather than generic “GEO” software.

My shortlist

1. My first choice: OtterlyAI

For your stated use case, OtterlyAI is probably the first vendor I'd put through an enterprise proof-of-concept.

It runs a defined set of prompts repeatedly and stores the resulting AI answers, allowing you to analyze brand mentions, position, sentiment, share of voice, competitors and citations over time. It currently advertises coverage across ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Gemini, Copilot and Claude. Otterly Otterly

The important caveat for your requirement is that its historical dataset is not retroactive: it starts collecting when you create the prompts/report. So if you're asking, “Can we reconstruct how our brand appeared over the last three years?” the answer is generally no. If you're asking, “Can we establish an enterprise baseline now and maintain a longitudinal dataset going forward?” it's much more compelling. Otterly

It also has an API, CSV exports, reporting, workspaces and Looker Studio integration, which matter considerably more at enterprise scale than having a nice dashboard. Otterly Otterly

2. Peec AI — particularly interesting for brand perception

Peec AI would be my other serious evaluation.

Its analytics distinguish brand visibility—whether the brand is explicitly mentioned—from source visibility—whether your domain/content is used or cited. That's a useful distinction for an enterprise brand team because “AI knows about us” and “AI trusts our content as a source” are quite different things. Peec.ai Docs

It tracks visibility, position, sentiment and share of voice and lets you examine trends over time and compare competitors. Peec.ai Docs Peec AI

Its newer Brand Perception capability is particularly relevant if your question isn't merely “How often is Nike mentioned?” but “What does AI believe Nike is good/bad/expensive/innovative/etc. at?” Peec explicitly surfaces the attributes AI associates with a brand and traces the claims driving those perceptions. Peec AI

What I'd require in an enterprise RFP

I wouldn't buy based on the number of “AI engines” a vendor says it supports. I'd make vendors demonstrate these capabilities against your actual prompt corpus:

• Prompt-level historical snapshots — preserve the actual AI response, not just a score.
• Model/version identification — e.g. which underlying model/version produced the response.
• Repeatability — timestamps, geography, language, logged-in/logged-out state and other conditions.
• Brand mention + position + sentiment + share of voice.
• Citation/source analysis — which websites caused the model to form its answer.
• Competitor benchmarking.
• Prompt taxonomy — product, category, comparison, reputation, purchase-intent, etc.
• Geographic segmentation — especially important if you're a multinational.
• API/data export — you should be able to put the raw observations into your own warehouse.
• Data retention and auditability.
• SSO/RBAC and enterprise security controls.
• Historical data ownership/export — make sure you can take the dataset with you if you leave.
• Model-change detection — distinguish a genuine brand-performance change from an underlying model/search-system change.

One important architectural point

If this is going to become a strategic enterprise measurement system, I'd avoid treating the vendor dashboard as your system of record.

I'd have the platform continuously collect observations and then push the underlying data into your own warehouse:

Prompt → model/search experience → timestamp → market → response → entities mentioned → ranking → sentiment → citations → competitors → model/version

Then your BI layer can answer questions such as:

“How has our share of AI voice changed by model, country, product category and quarter since September 2026?”

That gives you a much more defensible historical dataset than relying on a vendor's current aggregate “AI visibility score.”

Bottom line: I'd shortlist OtterlyAI and Peec AI, run the same 200–500 enterprise prompts through both for several weeks, and judge them primarily on raw-data accessibility, model/version fidelity, historical retention, reproducibility, and API quality. OtterlyAI has the edge for broad monitoring/citations; Peec is particularly compelling for brand perception and competitive intelligence. Otterly Peec AI

If you tell me roughly how many brands, markets, prompts, and AI models you need to monitor, I can also lay out an enterprise RFP scorecard and vendor comparison (including Profound, BrandRank, Scrunch, OtterlyAI, Peec, etc.).

Its newer Brand Perception capability is particularly relevant if your question isn't merely “How often is Nike mentioned?” but “What does AI believe Nike is good/bad/expensive/innovative/etc. at?” Peec explicitly surfaces the attributes AI associates with a brand and traces the claims driving those perceptions.

What I'd require in an enterprise RFP

I wouldn't buy based on the number of “AI engines” a vendor says it supports. I'd make vendors demonstrate these capabilities against your actual prompt corpus:

Yes. For an enterprise specifically looking for historical analytics of brand appearance across AI models, I’d prioritize dedicated AI visibility / LLM monitoring platforms rather than conventional SEO software.

My shortlist

1. Profound — my first choice

For your stated requirement, Profound is the one I'd evaluate first.

It is designed around enterprise AI visibility rather than simply adding an "AI mentions" feature to an SEO product. Its Answer Engine Insights tracks visibility/share of voice, sentiment, citation sources, competitors, topics, regions, personas and changes over time. It also queries the consumer-facing interfaces rather than simply making API calls, covering ChatGPT, Claude, Gemini, Perplexity, Copilot, Google AI Overviews/AI Mode, Grok and DeepSeek. Profound Built In

That's particularly important for a historical analytics program: you want a persistent dataset of prompt → model/surface → response → brand mention → position → sentiment → citations → competitors, rather than occasional snapshots.

tryprofound.com

2. Scrunch AI — worth putting through procurement

Scrunch AI is another strong enterprise candidate. Its monitoring covers major LLMs and Google AI Overviews and is explicitly positioned around measuring brand presence, performance and position across AI answers. Scrunch

I'd put it in an RFP alongside Profound if you need substantial scale or want to integrate AI visibility into a broader digital-experience/marketing stack.

scrunchai.com

3. Peec AI — good analytics-oriented alternative

Peec AI is attractive if the primary users will be SEO, content and marketing teams rather than a centralized enterprise intelligence function. It focuses heavily on brand mentions, sentiment, competitors and AI-search performance. Independent 2026 comparisons generally position it below Profound in enterprise depth but as a strong mid-market/multi-brand analytics option. Semrush Bloomiro

peec.ai

4. Semrush — best if you want AI + conventional search together

If your enterprise already uses Semrush, I'd seriously consider its LLM/AI visibility capabilities rather than buying another standalone platform. It combines LLM monitoring with conventional SEO, competitor and search data. Semrush

semrush.com

What I'd require in your RFP

This is actually more important than the vendor name. I'd require vendors to demonstrate these capabilities using your own historical use case:

• Persistent historical data — not just "last 30 days."
• Model-by-model history: ChatGPT, Claude, Gemini, Perplexity, Copilot, Google AI surfaces, etc.
• Prompt-level raw responses retained for auditability.
• Versioned prompts so you can distinguish a genuine visibility change from a prompt modification.
• Share of voice over time by brand, product, competitor and category.
• Citation/source history — exactly which websites caused the model to recommend or mention you.
• Sentiment and recommendation quality, not merely binary "mentioned/not mentioned."
• Competitor displacement: Who replaced us when we disappeared?
• Geography/persona segmentation.
• API/data export so your BI team can put the history into Snowflake/BigQuery/etc.
• Sampling methodology and reproducibility. This is crucial because LLM responses aren't deterministic.
• Model/surface distinction. A ChatGPT answer generated from memory should not necessarily be treated the same as a search-enabled ChatGPT answer with citations.
• Enterprise security: SSO/SAML, RBAC, audit logs, data retention controls and appropriate compliance certifications.
• Historical backfill, if available. Ask exactly how far back they can go; don't assume that "historical analytics" means they can reconstruct data from before you subscribed.

That last point is particularly important. These systems generally run and record their own standardized prompts over time; you shouldn't assume a vendor can magically reconstruct what ChatGPT said about your brand three years ago. The underlying measurement methodology matters enormously. Arbling AI Visibility Snapshot

My recommendation

If this is a serious enterprise-wide initiative, I'd run a 3-vendor bake-off: Profound vs. Scrunch AI vs. Peec AI, with Semrush as the incumbent-stack option if you already use it.

I'd make each vendor process the same 500–1,000 high-value prompts for your brand and 5–10 competitors, then compare:

historical fidelity → model coverage → raw-response access → citation intelligence → segmentation → API/export → governance → total cost.

For an enterprise buying this primarily as an analytics/intelligence system, rather than an SEO optimization tool, Profound would be my starting recommendation. Its current feature set is unusually aligned with the "how has our brand appeared across AI systems over time?" question. Profound

If you tell me roughly your industry, number of brands/products, geographic markets, and how many prompts/models you expect to monitor, I can turn this into an enterprise vendor scorecard/RFP matrix with weighted criteria and suggested questions for each vendor.

I'd put it in an RFP alongside Profound if you need substantial scale or want to integrate AI visibility into a broader digital-experience/marketing stack.

scrunchai.com

3. Peec AI — good analytics-oriented alternative

Peec AI is attractive if the primary users will be SEO, content and marketing teams rather than a centralized enterprise intelligence function. It focuses heavily on brand mentions, sentiment, competitors and AI-search performance. Independent 2026 comparisons generally position it below Profound in enterprise depth but as a strong mid-market/multi-brand analytics option. Semrush Bloomiro

peec.ai

4. Semrush — best if you want AI + conventional search together

If your enterprise already uses Semrush, I'd seriously consider its LLM/AI visibility capabilities rather than buying another standalone platform. It combines LLM monitoring with conventional SEO, competitor and search data. Semrush

Yes. For an enterprise, I’d look beyond a basic “AI visibility score” and prioritize historical response-level data, cross-model comparisons, prompt-level analytics, citations/sources, geographic segmentation, APIs, and governance.

My shortlist

1. enterprise.semrush.com — best overall enterprise choice

• Tracks visibility, mentions, citations and sentiment across major LLMs.
• Has historical insights, custom segmentation, competitive benchmarking and executive reporting.
• Particularly compelling if your organization already uses Semrush/SEO data because it connects AI visibility with website, search and traffic analytics. Semrush says its enterprise database covers 289M+ AI prompts, 39M+ brands and 15 regional AI-search databases. Semrush for Enterprise
• I'd start here for a large global marketing organization.
• tryprofound.com — best for dedicated AI/AEO intelligence

• Built specifically around enterprise answer-engine optimization rather than traditional SEO.
• Tracks how brands appear in AI answers, including prompt-level visibility and citation sources across major models. Built In
• A strong choice if your primary question is: “What is ChatGPT/Gemini/Perplexity telling prospective customers about us, and why?”
• It also appears particularly relevant if you want to analyze the sources AI systems use to construct their answers.
• spyderbot.net — interesting if you want model breadth + API

• Covers ChatGPT, Grok, Gemini, Copilot, Perplexity, Llama, Claude and others.
• Offers cross-model comparison, historical prompt monitoring, brand perception/competitive positioning and an enterprise API. SpyderBot
• I'd investigate this if your enterprise wants to feed AI-visibility data into its own BI/data warehouse rather than live entirely inside a SaaS dashboard.
• zumihq.com — good focused monitoring option

• Tracks up to nine AI engines, including ChatGPT, Gemini, Perplexity, Claude, Copilot, Grok, Google AI Overviews/AI Mode and DeepSeek.
• Runs tracked prompts daily and maintains history by brand, region, competitor and prompt. Zumi
• Worth considering if you primarily need reliable ongoing measurement, rather than a large optimization suite.

What I'd buy for your use case

If the requirement is specifically:

“Give me a historical record of how our brand has appeared across AI models over time.”

I'd evaluate Semrush Enterprise AIO and Profound first, then put SpyderBot in the technical/API comparison.

The key distinction is that you don't just want a chart saying “visibility = 63.” You want the underlying observations:

Date → model → prompt → full response → brand mention → position → sentiment/perception → competitors → citations/sources → market/language

That lets you answer questions such as:

• When did our visibility in ChatGPT change?
• Did Gemini start recommending a competitor instead of us?
• Which product categories are we losing?
• What sources are causing the models to describe us differently?
• Did a PR campaign/content launch actually change AI perception?
• How does our AI share of voice compare with competitors?
• Are changes consistent across models or specific to one model?
• Can we reconstruct what an AI answer looked like six months ago?

I'd also make raw-response retention and historical backfill a procurement requirement. Some platforms advertise “history,” but the important question is whether they retain the actual historical responses and citations, rather than only aggregated scores. For example, newer platforms such as GetMentioned explicitly describe storing full responses and making metrics traceable back to the answer that generated them. GetMentioned

Enterprise procurement checklist

I'd put these in the RFP:

One caution: AI visibility data isn't directly equivalent to organic search rankings. Responses can vary based on model, location, personalization, system changes and sampling methodology. Your vendor should therefore disclose exactly how prompts are executed and how historical measurements remain comparable. This is especially important when you're using the data for executive reporting.

If this is for a Fortune 500-style deployment, my initial ranking would be: Semrush Enterprise AIO → Profound → SpyderBot → Zumi. Semrush looks strongest as a broad enterprise marketing/data platform; Profound is the one I'd scrutinize most closely if pure AI-answer intelligence is the core mission. Semrush for Enterprise Built In SpyderBot Zumi

I'd put these in the RFP:

Yes. For an enterprise that wants historical, longitudinal analytics of how its brand appears across AI models, I’d treat this as an AI visibility / generative search intelligence platform rather than a conventional SEO tool.

My shortlist

My first vendor evaluation would be Profound. Independent 2026 comparisons consistently position it at the enterprise end of the category, while Profound's own documentation shows that its visibility analytics can be segmented by model, region, topic and time period, including share-of-voice and campaign comparisons. Profound Knowledge Base Aelo

Why I like these for your use case

The key distinction is that you don't merely want to know "Does ChatGPT mention us?" You want a historical dataset answering things like:

• How did our AI visibility change month-over-month?
• Which models are gaining or losing visibility for us?
• Which competitors are displacing us?
• What percentage of relevant prompts mention us?
• Where do we rank when multiple brands are recommended?
• Is the model describing us positively or negatively?
• Which websites/content are being cited when we're mentioned?
• Did a campaign, PR event, product launch, or content change affect AI visibility?
• How does our visibility differ by country, product, topic, and model?
• Can we export the underlying observations into our data warehouse/BI environment?

That last point is particularly important for an enterprise deployment.

Otterly is worth a serious evaluation

otterly.ai has unusually clear documentation around the historical data model. It runs tracked prompts daily across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude, and retains the resulting observations from the point a prompt begins being monitored. Otterly Otterly

It also provides brand coverage, share of voice, average position, sentiment, citations and competitor comparisons, with API access for pushing the data into systems such as Power BI, Tableau, BigQuery or Snowflake. Otterly Otterly

One caveat: none of these platforms can magically reconstruct historical AI responses from before they began monitoring your prompts. Otterly explicitly says it doesn't backfill historical prompt data. Otterly That's an important procurement question to ask every vendor.

Peec is another strong candidate

peec.ai focuses heavily on visibility, position, sentiment and share-of-voice over time, with the ability to select the AI models relevant to your category. Peec AI It also has increasingly sophisticated tracking around Google AI Mode, including citation/source intelligence and competitive benchmarking. Peec AI

What I'd require in an enterprise RFP

I'd actually make these requirements more important than the vendor's headline "number of models":

1. Raw response retention — ability to retrieve the actual AI response, not merely a visibility score.
2. Prompt-level history — every observation timestamped so you can reconstruct changes.
3. Model/version identification — e.g. distinguish model families and versions where possible.
4. Citation-level data — exactly which URLs/domains influenced the answer.
5. Competitor benchmarking — same prompts, same methodology.
6. Geographic/locale segmentation — AI results can vary materially by market.
7. API + warehouse integration — Snowflake/BigQuery/etc.
8. SSO/RBAC/audit controls — important for enterprise governance.
9. Export of underlying observations — avoid being trapped inside a proprietary dashboard.
10. Methodology transparency — particularly whether they use APIs, public interfaces, or other collection mechanisms.
11. Historical retention guarantees — contractually specify how long raw observations are retained.
12. Statistical consistency — fixed prompt sets, sampling methodology and controls for nondeterministic model responses.

That last issue is really important. AI responses aren't deterministic, so a dashboard saying your visibility went from 42% to 48% isn't necessarily meaningful unless the vendor explains its sampling and measurement methodology.

My recommendation

For a serious enterprise procurement, I'd run a 3-way proof of concept: Profound + Peec + Otterly.

Give each vendor the same 500–2,000 commercially important prompts, your top 20 competitors, 5–10 markets, and a requirement to retain the underlying responses. Run it for 4–6 weeks and compare:

data fidelity → historical analytics → model coverage → raw-data access → API → governance → reporting → cost.

If your objective is ultimately to build an enterprise "AI brand intelligence" data asset, rather than simply give marketers another dashboard, I'd weight raw historical data + API/warehouse access + methodological transparency more heavily than UI or the number of models advertised.

Yes. If the requirement is specifically historical, enterprise-grade measurement of how your brand appears across multiple AI models, I’d shortlist Profound first, with Scrunch AI and OtterlyAI as alternatives.

The important distinction is that you don't just want an “AI SEO” tool—you want a longitudinal measurement system that can preserve model responses, citations, competitors, sentiment, and share-of-voice over time.

My shortlist

Current industry comparisons consistently put Profound toward the enterprise end of this market, while Otterly emphasizes broad daily tracking and Scrunch combines visibility measurement with an agent-experience layer. Bloomiro TechnologyAdvice Otterly

Why I'd start with Profound

For an enterprise, I'd prioritize historical data integrity over the prettiest visibility score.

You want to be able to answer questions such as:

• “How often did ChatGPT mention us in Q1 vs. Q3?”
• “Which competitors gained share of voice?”
• “Which sources are causing the models to recommend our competitor?”
• “Did our sentiment improve after the product launch?”
• “Which prompts/models/markets are deteriorating?”
• “What URLs are being cited by AI models?”
• “Can we reconstruct what the model actually said six months ago?”

Profound is particularly interesting because its enterprise positioning is centered around deeper AI-search/citation analytics rather than simply producing a single “AI visibility score.” Independent 2026 comparisons also identify it as the enterprise-depth option. Bloomiro TechnologyAdvice

One thing I'd be very careful about

Don't evaluate vendors primarily on the number of AI models they claim to cover.

For historical analytics, I'd make vendors demonstrate:

1. Raw response retention — Can you retrieve the actual historical answer, not merely an aggregated score?
2. Model/version tracking — Can you distinguish model changes from genuine changes in brand visibility?
3. Prompt reproducibility — Can you see exactly which prompts generated each observation?
4. Citation history — Which URLs/domains were cited at each point in time?
5. Geography/language — Can you measure US vs. UK vs. Germany, etc.?
6. Competitor benchmarking — Same prompts, same models, same time periods.
7. API/data export — Can your data warehouse retain the underlying observations?
8. Enterprise security — SSO/SAML, RBAC, auditability, SOC 2, contractual data controls.
9. Change detection — Alerts when your visibility or sentiment changes materially.
10. Model coverage longevity — What happens when OpenAI, Google, Anthropic, etc. change their interfaces?

That's especially important because AI-answer behavior can change rapidly. For example, recent research has found major changes in which sources ChatGPT cites, illustrating why a six-month historical series needs to preserve the underlying observations rather than just today's aggregate score. Business Insider

What I'd buy for an enterprise

I'd probably run an RFP with Profound + Scrunch AI + OtterlyAI, and potentially Peec as the fourth bidder.

My initial ranking would be:

1. Profound — enterprise analytics / historical intelligence
2. Scrunch AI — analytics + technical optimization
3. OtterlyAI — broad, comparatively straightforward monitoring
4. Peec AI — additional benchmark for the RFP

Otterly is actually quite compelling if you need operational breadth: it currently says it tracks ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Gemini, Copilot and Claude, with daily mentions, sentiment, share-of-voice and citation tracking, plus API/export capabilities. Otterly

One caveat: I would not sign a multiyear contract based on a vendor's proprietary “AI visibility score.” I'd negotiate for raw historical observations + API access + export rights. That gives your enterprise the option to build its own canonical AI-visibility dataset rather than becoming dependent on whatever scoring methodology the vendor uses in 2027.

If you tell me roughly how many brands, countries, prompts, and AI models you need to monitor, I can turn this into a proper enterprise RFP scorecard (30–40 criteria) and recommend the best vendor/architecture for your scale.

Yes. For an enterprise, I’d treat this as AI visibility / LLM visibility intelligence, rather than ordinary brand monitoring. The key requirement is preserving the underlying prompt/response data so you can analyze how your brand’s representation changes over months and quarters—not just looking at a current “visibility score.”

My shortlist

• tryprofound.com — my first vendor to evaluate. It is one of the more established enterprise-oriented AI-search visibility platforms and is particularly relevant if you want large-scale prompt tracking, competitive share-of-voice, citations, and historical analysis. A recent 2026 comparison found Profound was the tool most frequently recommended by the AI models tested. OTW Motion
• meltwater.com — particularly compelling if your enterprise already uses Meltwater for PR, communications, media intelligence, and brand monitoring. Its GenAI Lens tracks brand/product/competitor mentions across ChatGPT, Gemini, Claude, Perplexity, Grok and DeepSeek and captures the responses and citations driving the narrative. Meltwater
• airops.com — worth considering if the objective isn't merely measurement but connecting AI visibility data to content optimization and execution. AirOps
• semrush.com — a sensible choice if your organization already has a large SEO/content intelligence investment in Semrush and wants AI visibility integrated into that ecosystem. AirOps
• presenc.ai — interesting specifically for your requirement around historical analytics. It explicitly tracks brand mentions across ChatGPT, Perplexity, Claude and Gemini over weeks, months and quarters, with daily/weekly/monthly trends. Presenc AI

What I'd require in an enterprise RFP

I'd make these non-negotiable:

One especially important question for vendors: are they querying the actual consumer-facing AI products or APIs? Those can produce materially different observations. Some platforms explicitly emphasize live-interface querying rather than relying solely on API responses. Promptwatch

My recommendation

For a large enterprise, I'd run a bake-off between Profound, Meltwater, and AirOps, with Presenc AI as a specialist contender if historical analytics is the primary requirement.

And I would not select based on whoever has the biggest list of supported models. The more important question is whether the vendor can give you a defensible longitudinal dataset:

Prompt → model/version → region → date/time → complete response → brand mentions → competitors → citations → sentiment → position

That dataset becomes substantially more valuable than the dashboard itself because you can put it into your own warehouse and answer questions like “Did our Q2 product launch increase recommendation share in ChatGPT among enterprise-software prompts?”

If you tell me roughly company size, industry, number of brands/markets, and whether you need API/data-warehouse access, I can narrow this to a 3-vendor enterprise shortlist and give you an RFP scorecard with suggested weighting and questions to ask each vendor.

Yes. For an enterprise specifically interested in historical analytics of how a brand appears across AI models/search experiences, I’d put Profound at the top of the shortlist, with Scrunch and OtterlyAI as strong alternatives.

My shortlist

1. Profound — my first choice

tryprofound.com

Profound is particularly well aligned with your requirement. Its Answer Engine Insights product builds a dataset from responses generated by AI answer engines and tracks visibility, citations, sentiment, share of voice, positioning, and trends. Its prompts are run daily, allowing you to build a longitudinal dataset rather than just taking a snapshot. Profound Knowledge Base Profound Knowledge Base

For an enterprise, I'd particularly investigate:

• Historical share of voice by model, topic, geography, and competitor
• Changes in how the model positions/describes your brand
• Citation sources and how those change over time
• Competitor movement
• Prompt-level response history
• API/data access for your BI warehouse
• Enterprise permissions, SSO, governance and procurement requirements
• Ability to separate branded vs. non-branded queries

Why I'd pick it: you're describing an AI brand intelligence dataset, not merely an "AI SEO rank tracker." Profound is oriented toward the former.

2. Scrunch — strongest alternative for custom analytics

scrunchai.com

Scrunch is interesting if your analytics team wants to build its own views rather than relying entirely on vendor-defined dashboards.

Its Explorer workspace supports custom metrics, aggregations, entity levels, filters and reusable dashboards, including period-over-period citation analysis and sentiment/competitor comparisons. Scrunch Help Center

That makes it attractive if you want questions such as:

"Show our AI visibility for enterprise buyers in North America, by product category, versus our five largest competitors, with the change since Q1."

rather than just:

"What's our AI visibility score?"

Scrunch also explicitly positions security/compliance, SSO, RBAC, scalability, API/export and multi-brand management as enterprise evaluation criteria. Scrunch

3. OtterlyAI — very good monitoring/data option

otterly.ai

OtterlyAI tracks brand mentions, citations, sentiment and share of voice across major AI search experiences, with daily monitoring. Otterly Otterly

One particularly relevant capability for an enterprise analytics team is its API, which can push visibility, citation and share-of-voice data into tools such as Tableau, Power BI, Looker Studio, BigQuery and Snowflake. Otterly

There is one important caveat for your use case: it doesn't backfill history. Historical data begins when you start monitoring a prompt. Otterly That's true of much of this category, because you generally can't reconstruct what an AI system answered months ago.

So if historical analytics is strategically important, start collecting the data now, even while you're evaluating vendors.

What I'd require in an enterprise RFP

I wouldn't select based simply on "number of AI models supported." I'd make vendors demonstrate these capabilities against your actual brand:

1. Longitudinal response archive — retain the actual AI responses, not merely scores.
2. Model-level history — ChatGPT, Gemini, Claude, Perplexity, Google AI experiences, Copilot, etc.
3. Prompt-level history — see exactly what changed for individual queries.
4. Share of voice — yours vs. every meaningful competitor.
5. Position/ranking — first recommendation vs. fifth recommendation matters.
6. Sentiment and positioning — what the model says about you, not just whether it mentions you.
7. Citation intelligence — which publishers/websites are causing the model to recommend you or competitors.
8. Geographic/market segmentation — especially important if you're multinational.
9. Product/entity-level tracking — brand → product → category → competitor.
10. Raw-data/API access — don't let the vendor's dashboard become your system of record.
11. Historical exports — ideally warehouse the underlying observations yourself.
12. Auditability — preserve model, prompt, timestamp, response and citations for every observation.

That last point is crucial. AI responses are probabilistic and change over time, so an enterprise shouldn't just store a daily "visibility score." You want the underlying observations so that six months from now you can answer why the score changed.

My recommendation

If I were running the enterprise procurement process, I'd do a 3-way bake-off: Profound vs. Scrunch vs. OtterlyAI, using the same ~100–500 strategically important prompts and your top 10–20 competitors.

I'd lean Profound if the primary goal is executive/brand intelligence, Scrunch if your analytics organization wants highly customizable reporting, and OtterlyAI if API/data integration and breadth of AI-search monitoring are especially important.

One other distinction I'd make: "historical analytics" and "historical research" are different requirements. If you need to know how your brand appeared in AI answers going back 12–24 months, ask vendors to show actual archived observations—not a retrospectively calculated score. That distinction can materially change which platform is suitable. Otterly Profound Knowledge Base

"What's our AI visibility score?"

Scrunch also explicitly positions security/compliance, SSO, RBAC, scalability, API/export and multi-brand management as enterprise evaluation criteria. Scrunch

3. OtterlyAI — very good monitoring/data option

otterly.ai

OtterlyAI tracks brand mentions, citations, sentiment and share of voice across major AI search experiences, with daily monitoring. Otterly Otterly

One particularly relevant capability for an enterprise analytics team is its API, which can push visibility, citation and share-of-voice data into tools such as Tableau, Power BI, Looker Studio, BigQuery and Snowflake. Otterly

There is one important caveat for your use case: it doesn't backfill history. Historical data begins when you start monitoring a prompt. Otterly That's true of much of this category, because you generally can't reconstruct what an AI system answered months ago.

So if historical analytics is strategically important, start collecting the data now, even while you're evaluating vendors.

What I'd require in an enterprise RFP

I wouldn't select based simply on "number of AI models supported." I'd make vendors demonstrate these capabilities against your actual brand:

1. Longitudinal response archive — retain the actual AI responses, not merely scores.
2. Model-level history — ChatGPT, Gemini, Claude, Perplexity, Google AI experiences, Copilot, etc.
3. Prompt-level history — see exactly what changed for individual queries.
4. Share of voice — yours vs. every meaningful competitor.
5. Position/ranking — first recommendation vs. fifth recommendation matters.
6. Sentiment and positioning — what the model says about you, not just whether it mentions you.
7. Citation intelligence — which publishers/websites are causing the model to recommend you or competitors.
8. Geographic/market segmentation — especially important if you're multinational.
9. Product/entity-level tracking — brand → product → category → competitor.
10. Raw-data/API access — don't let the vendor's dashboard become your system of record.
11. Historical exports — ideally warehouse the underlying observations yourself.
12. Auditability — preserve model, prompt, timestamp, response and citations for every observation.

That last point is crucial. AI responses are probabilistic and change over time, so an enterprise shouldn't just store a daily "visibility score." You want the underlying observations so that six months from now you can answer why the score changed.

My recommendation

If I were running the enterprise procurement process, I'd do a 3-way bake-off: Profound vs. Scrunch vs. OtterlyAI, using the same ~100–500 strategically important prompts and your top 10–20 competitors.

I'd lean Profound if the primary goal is executive/brand intelligence, Scrunch if your analytics organization wants highly customizable reporting, and OtterlyAI if API/data integration and breadth of AI-search monitoring are especially important.

One other distinction I'd make: "historical analytics" and "historical research" are different requirements. If you need to know how your brand appeared in AI answers going back 12–24 months, ask vendors to show actual archived observations—not a retrospectively calculated score. That distinction can materially change which platform is suitable. Otterly Profound Knowledge Base

Yes. For an enterprise, I’d focus on AI visibility / Answer Engine Optimization (AEO) platforms that continuously capture model outputs, rather than generic social-listening software.

My shortlist

1. My first choice: tryprofound.com

For a large enterprise, Profound is the one I'd evaluate first.

Its Answer Engine Insights product continuously queries AI answer engines and builds a dataset around your brand. You can analyze visibility, share of voice, sentiment, positioning, citations, competitors, topics, models, regions and audience personas over time. Profound Knowledge Base Profound

It also explicitly supports the front-end experiences users actually see—including ChatGPT, Claude, Gemini, Perplexity, Copilot, Google AI Overviews/AI Mode, Grok and DeepSeek—rather than simply relying on model APIs. Profound

The particularly interesting enterprise feature is its enormous underlying dataset: its 2026 Profound Index says it is based on 1.9+ billion real user conversations across 50+ industries and major answer engines. Profound

That's valuable if your question isn't merely:

"How does ChatGPT answer our 500 tracked prompts?"

but instead:

"How is our brand's AI visibility changing relative to the market, and what does the broader population of AI searches tell us?"

2. peec.ai

I'd put Peec very high on the evaluation list if your organization is particularly analytics/BI oriented.

It provides visibility, position, sentiment and share-of-voice metrics, with segmentation by model, country and prompt/audience tags. It says prompts are executed daily, giving you comparable trend data across models, dates and regions. Peec Peec

A major enterprise advantage is the ability to pipe the data into your existing stack through a REST API, Looker Studio connector or MCP. Peec

So if you want to build something like:

AI visibility → model → market → product → customer segment → time → competitor

inside your own data warehouse/BI environment, I'd take Peec seriously.

3. otterly.ai

Otterly is worth considering if you want broad monitoring without jumping immediately to the heaviest enterprise platform.

It monitors AI search systems including ChatGPT, Google AI Overviews, Perplexity, Gemini and Copilot, and provides prompt-level metrics such as brand coverage, sentiment, mentions, citations and competitors. OtterlyAI OtterlyAI

One important caveat for your particular requirement: it cannot reconstruct historical AI answers from before you started monitoring a prompt. Historical analytics begin when the prompt is created. OtterlyAI

That's actually true of this category generally: you should distinguish historical trend data collected continuously from claims that a vendor can somehow reconstruct what models said years ago.

What I'd require in an enterprise RFP

I'd make these non-negotiable:

• Model-level history: separate trends for ChatGPT, Claude, Gemini, Perplexity, Copilot, Google AI Overviews/AI Mode, etc.
• Prompt-level raw responses: don't accept only an aggregate "visibility score."
• Historical snapshots: ability to retrieve exactly what the model answered at a given point in time.
• Model/version tracking: distinguish changes caused by model updates from changes in your brand's underlying visibility.
• Share of voice: your brand vs. named competitors.
• Sentiment + narrative: not just whether you're mentioned, but how you're described.
• Citation/source analysis: which websites are influencing the answer.
• Geography: country/market-level differences.
• Persona/intent segmentation: informational vs. commercial vs. transactional prompts.
• API/data export: preferably raw response-level data, not just dashboard exports.
• Enterprise security: SSO/SAML, RBAC, audit logging, data retention controls and contractual data-processing terms.
• Reproducibility: methodology for handling stochastic model responses and changing model versions.

The important distinction

If your goal is "give me a dashboard showing how our brand appears in AI", Peec or Otterly may be sufficient.

If your goal is "build an enterprise-grade historical intelligence system showing how AI perceptions of our brand have evolved across models, markets and competitors", I'd shortlist Profound + Peec, with Profound probably getting the first enterprise proof-of-concept.

I'd also insist that the POC demonstrate 90–180 days of continuously collected data, because "historical analytics" is where many AI-visibility products differ substantially. Profound's breadth of model/market analysis and Peec's API/BI orientation make them particularly interesting complementary candidates. Profound Peec

If you tell me your approximate number of brands/products, countries, prompts, and whether you need raw data/API access, I can narrow this to a 2–3 vendor enterprise shortlist and give you an RFP-style comparison of capabilities, architecture, data ownership, integrations, and likely pricing model.

That's valuable if your question isn't merely:

"How does ChatGPT answer our 500 tracked prompts?"

but instead:

"How is our brand's AI visibility changing relative to the market, and what does the broader population of AI searches tell us?"

2. peec.ai

I'd put Peec very high on the evaluation list if your organization is particularly analytics/BI oriented.

It provides visibility, position, sentiment and share-of-voice metrics, with segmentation by model, country and prompt/audience tags. It says prompts are executed daily, giving you comparable trend data across models, dates and regions. Peec Peec

A major enterprise advantage is the ability to pipe the data into your existing stack through a REST API, Looker Studio connector or MCP. Peec

So if you want to build something like:

AI visibility → model → market → product → customer segment → time → competitor

inside your own data warehouse/BI environment, I'd take Peec seriously.

3. otterly.ai

Otterly is worth considering if you want broad monitoring without jumping immediately to the heaviest enterprise platform.

It monitors AI search systems including ChatGPT, Google AI Overviews, Perplexity, Gemini and Copilot, and provides prompt-level metrics such as brand coverage, sentiment, mentions, citations and competitors. help.otterly.ai help.otterly.ai

One important caveat for your particular requirement: it cannot reconstruct historical AI answers from before you started monitoring a prompt. Historical analytics begin when the prompt is created.

That's actually true of this category generally: you should distinguish historical trend data collected continuously from claims that a vendor can somehow reconstruct what models said years ago.

What I'd require in an enterprise RFP

I'd make these non-negotiable:

Yes. If the requirement is specifically historical analytics of how your brand is represented across multiple AI models, I’d prioritize purpose-built AI-visibility platforms over traditional brand-monitoring software.

My enterprise shortlist

My first evaluation would be Profound. It is unusually well aligned with your requirement: it tracks visibility, share of voice, sentiment, citations, competitors and accuracy, and lets you examine how those metrics change over time. It also says its measurements come from the consumer-facing AI experiences rather than simply API outputs. Profound Profound

That distinction matters. If you're asking "What does our brand actually look like to someone using ChatGPT or Claude?", I'd want a platform measuring the front-end experience rather than treating an API response as equivalent.

Why Profound stands out

For an enterprise analytics program, I'd want at least these dimensions:

• Model-by-model trends — e.g. brand visibility in ChatGPT vs. Claude vs. Gemini.
• Share of voice — your brand versus competitors.
• Sentiment and narrative — not merely whether you're mentioned, but how you're described.
• Citation/source analysis — which publications and websites are causing AI systems to represent you a certain way.
• Prompt-level history — exactly which questions produce positive, negative, or missing mentions.
• Geographic/persona segmentation — whether representation differs by market or audience.
• Accuracy monitoring — detection of incorrect claims about the brand.
• Historical snapshots — so you can correlate changes with campaigns, PR events, product launches, SEO/content changes, etc.

Profound currently advertises essentially this complete stack, including daily visibility runs and historical analysis across major consumer AI experiences. Profound

Where BrightEdge makes more sense

If your organization already has a substantial BrightEdge deployment, I'd seriously consider extending it rather than introducing another analytics platform.

BrightEdge now combines traditional SEO with AI-search analytics, including brand mentions/citations, sentiment, competitive visibility and historical search data. It also has substantial enterprise penetration—its own materials cite more than 8,500 brands and 57% of the Fortune 100. BrightEdge BrightEdge

The tradeoff is that Profound is more AI-native, whereas BrightEdge is particularly compelling if you want one system spanning SEO + AI search.

Presenc AI is worth a look

If your primary requirement is "give me a defensible historical time series of our AI brand presence", Presenc AI is interesting. Its historical analytics specifically track changes in brand mentions across ChatGPT, Perplexity, Claude and Gemini over weeks, months and quarters. Presenc AI

I'd put it through an enterprise proof-of-concept alongside Profound rather than making it the default choice.

What I'd demand in an enterprise RFP

The biggest thing I'd test isn't the dashboard—it's the underlying measurement methodology.

Ask every vendor:

1. Are responses collected from consumer-facing AI products or APIs?
2. How do you handle stochastic/model-variable answers?
3. Can we reproduce an historical measurement?
4. What is actually stored for each observation—prompt, response, model, timestamp, geography, citations?
5. Can we export the raw observation data into our warehouse?
6. Can we track 1,000s/10,000s of prompts?
7. Can we separate brand, product, corporate and executive entities?
8. Can we benchmark dozens of competitors?
9. Can we see changes at the model × market × prompt × time level?
10. What happens when an AI model changes its UI, model version, search behavior, or citation mechanism?
11. What's the retention period for historical observations?
12. Do they provide SSO, RBAC, audit logs, API access and enterprise data controls?

That last group is important because "AI visibility score" isn't yet a standardized metric. Two vendors can query ostensibly the same model and produce different scores because their prompts, sampling, geography, frequency and response-collection methodology differ.

My recommendation

For a large enterprise, I'd run a 2–4 week bake-off between Profound and BrightEdge, using the same controlled prompt corpus and requiring each vendor to deliver:

AI visibility → share of voice → sentiment → citations → competitor comparison → historical trend → raw observations → executive reporting.

If you are building a new AI analytics function, I'd lean Profound.

If you already have BrightEdge/enterprise SEO infrastructure, I'd lean BrightEdge for the unified stack.

If your central question is specifically "How has our brand's representation in ChatGPT/Claude/Gemini changed over the last 12–24 months?", I'd make historical data provenance and raw-response retention the deciding criterion rather than the number of features on the dashboard.

For an enterprise looking for historical analytics of how your brand appears across AI models, I would evaluate this as an AI visibility / Generative Engine Optimization (GEO) / Answer Engine Optimization (AEO) intelligence requirement rather than a traditional social listening problem. The strongest platforms typically track:

• Brand mentions and recommendations inside LLM answers
• Share of voice vs. competitors
• Sentiment and positioning
• Citation sources (which websites, articles, communities, etc. influence the answer)
• Prompt-level history over time
• Model-by-model differences (ChatGPT vs. Gemini vs. Claude vs. Perplexity, etc.)
• Alerts when brand perception changes Built In Meltwater

My shortlist for enterprise use:

My recommendation by enterprise scenario

If you are a global brand managing reputation

Start with Profound + Meltwater.

• Profound gives you the AI-answer analytics layer.
• Meltwater gives PR teams a familiar reputation-monitoring workflow.
• Together they cover “What are AI models saying?” and “How does this relate to broader brand perception?” Built In Meltwater

If your goal is marketing growth and AI search acquisition

Look at Profound, Scrunch AI, or Semrush.

Key capabilities to demand:

• Thousands of tracked prompts
• Competitor benchmarking
• Historical trend lines
• Citation gap analysis
• Export/API access
• Role-based dashboards Built In

If you need governance, auditability, and executive reporting

Prioritize:

• Data retention policies
• Model coverage transparency
• Repeatable prompt sets
• Evidence snapshots of AI responses
• SSO/SAML and enterprise security controls
• API access into BI tools

Questions I would ask vendors before buying

1. Which models do you actually monitor?

• ChatGPT
• Gemini
• Claude
• Perplexity
• Copilot
• AI Overviews
• Emerging models
• Do you store historical responses?

• Can we replay what an AI model said six months ago?
• Are screenshots/raw outputs retained?
• How do you normalize model differences?

• A “mention” in one model is not always equivalent to another.
• Can we upload our own prompt universe?

• Enterprise teams usually need hundreds or thousands of customer-intent prompts.
• Can we compare against competitors?

• “How often is our brand recommended vs. competitor X for buying-intent questions?”
• Can data feed our warehouse/BI stack?

• Look for API, Snowflake/BigQuery connectors, or scheduled exports.

For a large enterprise procurement process, I would probably run a proof-of-concept with Profound, Meltwater GenAI Lens, and Scrunch AI using the same 500–1,000 customer prompts and compare:

• coverage,
• historical accuracy,
• citation tracking,
• competitor insights,
• executive reporting quality. Built In Meltwater

I can also provide a vendor evaluation scorecard/RFP template if you are taking this through procurement.

Yes. For an enterprise that specifically wants historical analytics of how its brand appears across AI models, I’d focus on platforms built for AI visibility / answer-engine analytics, rather than generic social-listening or SEO tools.

My shortlist

My first vendor evaluation would be Profound. Current 2026 comparisons consistently position it as the enterprise-oriented option, particularly for deep reporting, citation/source analysis and large-scale prompt tracking. Its Enterprise offering reportedly covers up to roughly 10 answer engines, including ChatGPT, Perplexity, Gemini, Google AI Overviews/AI Mode, Copilot, Meta AI, Grok, DeepSeek and Claude. baarely.combloomiro.com

What I'd require in an enterprise RFP

The important distinction is that you don't merely want to know "did ChatGPT mention us?" You want a longitudinal dataset that lets you answer:

• Mention rate: What percentage of relevant prompts mention our brand?
• Position/rank: Are we #1, #3, etc. when recommended?
• Share of voice: How do we compare with competitors?
• Sentiment / narrative: How does the model describe us?
• Citation share: Which of our URLs/content are being cited?
• Competitor displacement: When we disappear, who replaces us?
• Model differences: Does ChatGPT treat us differently from Gemini, Claude, Perplexity, etc.?
• Geographic differences: Does visibility change by country/market/language?
• Time series: Can we look back 3, 6, 12+ months?
• Raw answer preservation: Can we retrieve the actual AI responses behind the metrics?
• Prompt/version history: Can we distinguish changes in visibility from changes to the prompt set?
• API/export: Can the underlying observations go into Snowflake/BigQuery/BI?
• Enterprise governance: SSO/SAML, RBAC, audit logs, data retention and contractual data controls.

That raw-answer + historical-observation layer is particularly important. Otherwise, you're buying a dashboard rather than an enterprise intelligence dataset.

Profound vs. Peec

I'd put these two through a serious bake-off.

Profound — strongest candidate if this is becoming a corporate intelligence function involving brand, SEO, PR, product marketing and executives. Its enterprise positioning emphasizes large-scale monitoring, source/citation analysis and reporting. bloomiro.com

Peec AI — worth evaluating if your primary users are marketing/SEO teams and you want a cleaner analytics/benchmarking experience. It tracks visibility, competitors and multiple markets/models and is generally positioned below Profound in enterprise complexity. baarely.com

Otterly is a good third evaluation, especially if you want breadth without immediately committing to an enterprise platform. It currently tracks ChatGPT, Perplexity, Google AI surfaces, Gemini, Copilot and Claude, with daily tracking of mentions, sentiment, share of voice and citations. otterly.ai

One important caveat

I'd not select a vendor based simply on the number of "AI models" it claims to track.

There are really two different things:

1. Model-memory measurement — ask ChatGPT/Claude/etc. what they know about your brand.
2. AI-search measurement — measure the answers generated when the model has access to search/web sources.

Those can produce dramatically different results. A current 2026 comparison specifically notes that search-enabled versus memory-based surfaces can produce very different citation behavior. arbling.com

For an enterprise brand, I'd want your historical database to preserve which surface, model/version, search state, geography, prompt, date and sources produced each observation.

Bottom line: if you're buying this as enterprise infrastructure rather than an SEO team's dashboard, start with Profound, benchmark it against Peec, and use Otterly as the lower-cost control. I'd make the ability to export/retain the underlying historical observations—not just charts—the deciding criterion.

If you tell me roughly your industry, number of brands/markets, and whether you need API/Snowflake integration, I can also build you a more rigorous enterprise vendor comparison/RFP scorecard with ~15–20 criteria and recommended weights.

Yes. For an enterprise brand that needs historical, cross-model analytics, I’d shortlist Profound first, with Peec AI as the strongest alternative.

My recommendation

1. tryprofound.com — best enterprise fit

• Built around monitoring how brands are represented across AI answer engines.
• Tracks visibility, citations, sentiment, share of voice, positioning, and competitive performance. Profound Knowledge Base Profound Knowledge Base
• Covers major surfaces including ChatGPT, Claude, Gemini, Perplexity, Grok, Copilot, DeepSeek and Google AI Overviews. Profound
• Its prompt monitoring is designed for longitudinal measurement, with prompts queried regularly and results aggregated into a dataset you can analyze over time. Profound Knowledge Base
• Particularly attractive if you want more than a dashboard: Profound also has content/page analytics and optimization workflows. Profound Knowledge Base

I'd choose this if: you're a Fortune 1000/large multinational, need governance and reporting across brands/markets, and want AI visibility to become an enterprise marketing KPI.

2. peec.ai — best analytics-focused alternative

• Strong on visibility, position, sentiment, citations and competitor comparison. Peec
• Supports segmentation by model, country and prompt tags, which is useful for comparing markets, personas and funnel stages. Peec
• Explicitly runs prompts daily, giving you apples-to-apples trend data across dates, models and regions. Peec
• Has API, CSV, Looker Studio and MCP integrations, making it interesting if your enterprise wants to put the data into an existing BI/data stack. Peec

I'd choose this if: your primary requirement is a clean AI brand intelligence/analytics layer rather than a broader optimization platform.

3. otterly.ai — worth evaluating, but not my first enterprise choice

It is a legitimate AI-search monitoring option, but the current market positioning puts it more toward accessible monitoring/SEO workflows than the deepest enterprise analytics. Arbling Mention Radar

What I'd insist on in an enterprise RFP

The important distinction is that you don't merely want an "AI visibility score." You want the underlying historical observation data.

I'd require vendors to demonstrate:

• Raw response history: preserve the actual AI response associated with every measurement.
• Model/version tracking: know which model and, ideally, model version generated each observation.
• Prompt-level history: see exactly how a specific prompt's answer changed month over month.
• Cross-model comparison: ChatGPT vs. Gemini vs. Claude vs. Perplexity, etc.
• Share of voice: your brand vs. named competitors.
• Citation intelligence: which domains/pages/models are influencing the answers.
• Sentiment and positioning: not merely whether you're mentioned, but how you're described.
• Geography/language: especially important for multinational brands.
• Custom prompt taxonomies: product, category, consideration, competitor, reputation, etc.
• API/data export: don't let your historical dataset become trapped inside the SaaS UI.
• Stable methodology: the vendor should explain how it handles changes in model behavior, sampling, personalization and search results.
• Auditability: ability to reproduce or inspect a historical observation.
• Enterprise access controls: SSO/SAML, RBAC, audit logs and appropriate data-retention controls.
• BI integration: ideally warehouse/API access rather than relying exclusively on PDFs or dashboards.

One particularly important question

I'd ask every vendor:

"If our CMO asks why our AI visibility score changed from 42 to 31 on March 15, can you show me the underlying prompts, model responses, citations, competitors, methodology and historical baseline that produced that change?"

That separates a genuine enterprise intelligence platform from an SEO tool that happens to have an AI visibility score.

Bottom line: I'd run an enterprise bake-off between Profound and Peec AI, with Profound as the initial favorite. Profound appears to have the broader enterprise platform, while Peec is especially compelling if your core need is high-quality longitudinal analytics plus API/BI integration. Profound Peec

If you tell me roughly company size, number of brands/countries, and which models you care about, I can narrow this to a 3–5 vendor enterprise shortlist and give you an RFP scorecard with weighted criteria.

Yes. For an enterprise that specifically wants historical analytics of how its brand appears across multiple AI models, I’d put Profound at the top of the shortlist.

My enterprise shortlist

1. tryprofound.com — my first choice

Profound is the one I'd evaluate first for an enterprise deployment. Its Answer Engine Insights product maintains a dataset of responses generated by AI answer engines and lets you analyze visibility, share of voice, sentiment, positioning and citations. It supports prompt-driven monitoring on a recurring basis, which is important if you want a defensible historical time series rather than occasional spot checks. Profound Knowledge Base Profound

It also goes beyond "did the model mention us?" You can investigate which sources are influencing the answers, what the models are saying about your brand, competitive positioning, and inaccurate claims. Profound Profound Knowledge Base

For an enterprise, I'd particularly investigate:

• Historical visibility/share-of-voice by model
• Brand vs. competitor tracking
• Prompt/topic segmentation
• Citation/source tracking
• Sentiment and positioning changes
• Geographic or market segmentation
• API/data export and integration options
• Permissions and enterprise governance
• Alerts when visibility materially changes
• Ability to preserve raw historical responses, not just aggregate scores

Profound also has Agent Analytics, which is interesting if you want to connect model visibility with actual AI crawler activity and AI-driven site traffic. Profound

2. peec.ai — strong analytics alternative

I'd put Peec second. Its core analytics are particularly clean: visibility, position, sentiment and share of voice, with historical trends and competitor benchmarking. It tracks major AI surfaces including ChatGPT, Gemini, Perplexity and Google's AI experiences. Peec AI Peec.ai Docs

If your organization already has sophisticated BI/data infrastructure and primarily wants reliable AI-visibility data to feed into that infrastructure, Peec is worth putting through an enterprise evaluation.

3. otterly.ai — good for broader rollout at lower cost

Otterly is worth considering if you want to instrument many brands/products/markets without immediately committing to a heavyweight enterprise platform. I would, however, favor Profound for a central enterprise intelligence program where historical data quality, analytical depth and governance matter more than price.

4. Semrush's AI visibility tooling

If your enterprise already has a large Semrush deployment, I'd evaluate its AI visibility capabilities alongside the dedicated vendors. The advantage isn't necessarily that it's the deepest AI analytics platform; it's that AI visibility can sit alongside your existing SEO, competitive and search datasets. Current industry comparisons generally place dedicated platforms such as Profound ahead for depth, while Semrush is attractive when consolidation matters. Acromatico Kitbase

The important enterprise distinction

I'd be careful about buying a tool merely because it says it "tracks ChatGPT."

For your use case, the underlying data model matters enormously. You want something roughly like:

Brand → market → topic → prompt → model/surface → date → response → mention → position → sentiment → competitor mentions → citations → source → change over time

That lets you answer questions such as:

"Over the last 12 months, how did our share of voice among AI-generated answers for enterprise cybersecurity prompts change, by model and region, and which publications drove the change?"

That's substantially more valuable than a dashboard saying "AI visibility = 63."

Also, don't conflate model coverage with answer-engine coverage. ChatGPT, Gemini, Claude, Perplexity, Google AI Overviews/AI Mode, Copilot, etc. can produce materially different answers and use different retrieval/citation mechanisms. Profound's current documentation explicitly distinguishes these answer engines and analyzes the resulting responses as a dataset. Profound Knowledge Base

My recommendation

For an enterprise procurement process, I'd run an RFP with Profound + Peec + your incumbent SEO platform and make the vendors prove the historical-data layer rather than just demo the dashboard.

I'd weight the evaluation approximately:

1. Historical/raw response data & reproducibility — 25%
2. Model/AI-surface coverage — 20%
3. Prompt/topic/market segmentation — 15%
4. Citation and source intelligence — 10%
5. Competitive benchmarking — 10%
6. API/data warehouse/BI integration — 10%
7. Security, SSO, permissions, governance — 10%

If I had to choose one today: Profound. Its combination of recurring prompt monitoring, historical answer datasets, multi-dimensional visibility analytics, citations, competitive analysis and enterprise-oriented capabilities is the closest match to the use case you described. Profound Knowledge Base Profound Knowledge Base

If you tell me roughly how many brands, countries/markets, prompts, and AI models you need to monitor, I can also give you an enterprise RFP-style comparison of Profound vs. Peec vs. Semrush vs. Otterly, including what I'd negotiate on pricing and what technical questions I'd ask each vendor.

Otterly is worth considering if you want to instrument many brands/products/markets without immediately committing to a heavyweight enterprise platform. I would, however, favor Profound for a central enterprise intelligence program where historical data quality, analytical depth and governance matter more than price.

4. Semrush's AI visibility tooling

If your enterprise already has a large Semrush deployment, I'd evaluate its AI visibility capabilities alongside the dedicated vendors. The advantage isn't necessarily that it's the deepest AI analytics platform; it's that AI visibility can sit alongside your existing SEO, competitive and search datasets. Current industry comparisons generally place dedicated platforms such as Profound ahead for depth, while Semrush is attractive when consolidation matters. Acromatico Kitbase

The important enterprise distinction

I'd be careful about buying a tool merely because it says it "tracks ChatGPT."

For your use case, the underlying data model matters enormously. You want something roughly like:

Brand → market → topic → prompt → model/surface → date → response → mention → position → sentiment → competitor mentions → citations → source → change over time

That lets you answer questions such as:

"Over the last 12 months, how did our share of voice among AI-generated answers for enterprise cybersecurity prompts change, by model and region, and which publications drove the change?"

That's substantially more valuable than a dashboard saying "AI visibility = 63."

Also, don't conflate model coverage with answer-engine coverage. ChatGPT, Gemini, Claude, Perplexity, Google AI Overviews/AI Mode, Copilot, etc. can produce materially different answers and use different retrieval/citation mechanisms. Profound's current documentation explicitly distinguishes these answer engines and analyzes the resulting responses as a dataset. Profound Knowledge Base

My recommendation

For an enterprise procurement process, I'd run an RFP with Profound + Peec + your incumbent SEO platform and make the vendors prove the historical-data layer rather than just demo the dashboard.

I'd weight the evaluation approximately:

1. Historical/raw response data & reproducibility — 25%
2. Model/AI-surface coverage — 20%
3. Prompt/topic/market segmentation — 15%
4. Citation and source intelligence — 10%
5. Competitive benchmarking — 10%
6. API/data warehouse/BI integration — 10%
7. Security, SSO, permissions, governance — 10%

If I had to choose one today: Profound. Its combination of recurring prompt monitoring, historical answer datasets, multi-dimensional visibility analytics, citations, competitive analysis and enterprise-oriented capabilities is the closest match to the use case you described.

Yes. For an enterprise, I’d treat this as AI visibility / answer-engine intelligence, not merely “LLM monitoring.” The important distinction is whether the platform preserves historical prompt-level observations, lets you segment results by model/market/topic, and can benchmark competitors over time.

My shortlist

1. Profound — my default recommendation

For your specific requirement—“How has our brand appeared across different AI models over historical periods?”—I'd start with Profound.

Its Answer Engine Insights tracks brand mentions, sentiment, citations and competitive visibility, while its prompt-tracking system captures AI-generated answers and citation sources daily, allowing you to analyze performance over time. Enterprise supports a broad set of answer engines. Profound Profound Profound

This is particularly useful if you want dashboards such as:

• Brand visibility by ChatGPT vs Gemini vs Claude vs Perplexity vs Google AI
• Share of voice by month/quarter
• Brand position over time
• Citation/source changes
• Positive/negative/neutral sentiment
• Competitor displacement
• Visibility by customer persona, geography or topic
• Which prompts are causing the biggest changes
• What changed between Q1 and Q2, rather than simply today's score

I'd make Profound the benchmark to beat in your RFP.

2. Adobe Brand Visibility — potentially the strongest enterprise option

Adobe launched Adobe Brand Visibility in 2026, combining Adobe's LLM optimization capabilities with Semrush's AI-search intelligence. It is aimed squarely at enterprises and provides historical competitive comparisons, share of voice, audience reach, prompt/topic intelligence and visibility across platforms including ChatGPT, Google AI Mode, Microsoft Copilot and Perplexity. Adobe Newsroom Adobe Partners

The interesting part for a large organization is the connection between AI visibility → content changes → business outcomes. Adobe says the platform can connect GEO activity to bookings, pipeline and revenue through its analytics ecosystem. Adobe Newsroom

I'd favor Adobe over Profound if your marketing organization is already heavily standardized on Adobe Experience Cloud.

3. BrightEdge AI Catalyst

If your enterprise already has a serious SEO organization, BrightEdge is compelling because AI visibility sits alongside conventional search rather than being another isolated SaaS dashboard.

AI Catalyst tracks visibility and sentiment across Google AI Overviews, ChatGPT and Perplexity and uses historical search/query data to help identify relevant AI prompts. BrightEdge says the platform uses billions of historical and real-time data points. BrightEdge BrightEdge

That makes it particularly attractive if leadership wants a unified:

Google Search + AI Search + brand visibility reporting layer.

What I'd actually buy

For a Fortune-500-ish enterprise, I'd run an RFP with:

1. Profound — benchmark for dedicated AI visibility
2. Adobe Brand Visibility — benchmark for enterprise marketing integration
3. BrightEdge AI Catalyst — benchmark for SEO + AI convergence
4. Conductor — additional enterprise alternative

I wouldn't make Otterly the primary enterprise system unless your requirements are relatively lightweight. It has useful historical views and broad engine coverage, but I'd want the enterprise vendors to demonstrate their data architecture and historical retention before selecting it as the corporate source of truth. Otterly

One thing I'd insist on in the RFP

Don't just ask vendors “Do you have historical analytics?”

Ask them to demonstrate this exact scenario:

“Show us every observation of our brand for 500 specified prompts across ChatGPT, Claude, Gemini, Perplexity and Google AI over the last 12 months, including the exact response, cited sources, competitors mentioned, sentiment, position, model/version, geography, timestamp, and prompt. Then show us how you calculate month-over-month changes and let us export the underlying observations.”

That exposes a major difference between real historical intelligence and a dashboard that merely stores aggregate visibility scores.

I'd also require raw response-level data and an API/export, because your enterprise will eventually want to put this into its own data warehouse alongside CRM, web analytics, SEO and revenue data.

If you tell me roughly your company size, number of brands/markets, and which models you care about, I can narrow this to a 2–3 vendor recommendation and give you an enterprise RFP scorecard (data coverage, historical depth, API, SSO/RBAC, governance, integrations, pricing, and accuracy).

Yes. For an enterprise, I’d focus less on “GEO score” and more on historical, model-level evidence: what prompts were asked, what each model answered, whether your brand was mentioned/cited, your position and sentiment, which competitors appeared, and how those metrics changed over time.

My shortlist

1. Profound — my first choice for a serious enterprise program.
Profound's Answer Engine Insights queries AI answer engines on a recurring basis and builds a dataset around your brand's visibility, citations, sentiment, share of voice, positioning and historical charts. Its visibility data can also be broken down across models, regions and topics, and its automation layer can trigger actions when visibility changes. help.tryprofound.com

That's particularly appropriate if you're trying to answer questions such as:

• “How has our share of voice in ChatGPT changed since Q1?”
• “Which models perceive us differently?”
• “Which competitors displaced us in high-value prompts?”
• “Did our visibility improve after the product launch?”
• “Which sources are causing Claude to favor a competitor?”

2. Peec AI — excellent if analytics/benchmarking is the priority.
Peec explicitly exposes visibility, position, sentiment, competitor comparisons and source/citation analysis, with daily fluctuations in its visibility graph. It also offers API access and CSV/Looker Studio export, which is useful if you want to put AI visibility into an existing enterprise BI environment. docs.peec.ai

It currently covers ChatGPT, Google AI Overviews/AI Mode, Perplexity, Gemini and Copilot, with additional models available as upgrades. peec.ai

3. OtterlyAI — strongest breadth/value alternative.
Otterly monitors ChatGPT, Google AI Overviews, Google AI Mode, Gemini, Perplexity, Copilot and Claude, and provides brand visibility, citations, competitors and historical trends. It also has an enterprise offering with higher usage limits, SSO/team management, dedicated support and custom integrations. otterly.ai

Its API is particularly interesting if your enterprise wants to pipe the data into its own data warehouse or dashboards. help.otterly.ai

What I'd buy

If this is a Fortune-100/large multinational brand, I'd run an RFP with Profound + Peec AI + OtterlyAI, but make Profound the benchmark to beat.

I'd specifically require vendors to demonstrate these capabilities using your prompts before signing:

1. Raw historical responses — not just a proprietary visibility score.
2. Model-by-model tracking — ChatGPT, Claude, Gemini, Perplexity, Copilot, Google AI experiences, etc.
3. Immutable historical snapshots so you can distinguish an actual model change from a measurement change.
4. Prompt versioning and the ability to preserve your benchmark set.
5. Brand + competitor share of voice over time.
6. Citation-level data — domain, URL, source type and position.
7. Sentiment/positioning changes over time.
8. Country/language segmentation for global brands.
9. API + warehouse export rather than forcing executives to live in another dashboard.
10. Auditability/reproducibility — ideally the ability to see exactly what was queried and what the model returned.

One important caveat: “historical analytics” in this category isn't the same as historical model telemetry. These products generally build their history by repeatedly querying models/AI-search experiences and storing the resulting answers. Profound, for example, describes its prompts as queries it sends to answer engines daily and aggregates those responses into its analytics dataset. help.tryprofound.com

So if your requirement is “tell us how our brand's AI visibility has evolved over the last 12–24 months,” these are appropriate. If instead you mean “give us a forensic archive of every version of GPT/Claude/Gemini and how the model itself changed its representation of our brand,” that's a substantially different—and more specialized—data problem.

If you tell me your approximate company size, number of brands/markets, and which models matter most, I can narrow this to a 2–3 vendor enterprise shortlist and give you an RFP scorecard with the specific questions I'd put to each vendor.

If by historical analytics you mean “show me, over months/years, how often our brand was recommended, mentioned, ranked, described, and cited across ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews, etc., including competitors”, I’d evaluate this as an AI visibility / GEO intelligence platform, not a generic social-listening product.

My enterprise shortlist

What I'd prioritize for your use case

For an enterprise, I'd make these non-negotiable requirements:

1. Immutable historical snapshots — you should be able to reproduce what the model answered on a particular date, not just see today's score.
2. Model/version tracking — distinguish GPT model A vs. GPT model B, rather than treating “ChatGPT” as one continuously stable source.
3. Prompt-level history — exact prompt → exact response → mentions/citations → timestamp.
4. Cross-model comparison — ChatGPT vs. Claude vs. Gemini vs. Perplexity, etc.
5. Share of voice — your brand vs. named competitors.
6. Citation/source intelligence — which websites are causing the model to mention competitors instead of you?
7. Sentiment and positioning — not merely “mentioned/not mentioned,” but how the brand is characterized.
8. Geography/persona/language segmentation — AI answers can vary materially based on query framing and market.
9. API/data export — important if you're going to put this into your enterprise BI/data warehouse.
10. Auditability — especially important if executives will use the numbers in quarterly reporting.

That last point is easy to underestimate. Recent research shows that AI recommendations can vary considerably between models and across repeated observations, so an enterprise dashboard should preserve the underlying observations rather than presenting a single opaque “AI visibility score.” arxiv.org

My recommendation

I'd put Conductor, Profound, Gumshoe and Quattr through a formal bake-off.

If your primary question is “How is our brand's AI reputation changing over time?”, I'd pay particular attention to Gumshoe because its model/persona/time-series approach maps closely to what you're asking for. gumshoe.ai

If you're a large SEO/marketing organization and want AI visibility + conventional search intelligence in one enterprise stack, I'd start with Conductor. A current 2026 evaluation also ranks it the enterprise leader among AI-visibility platforms. www.conductor.com

If you tell me roughly enterprise size (e.g. Fortune 500 vs. mid-market), number of brands/countries, and whether you need an API/data warehouse, I can narrow this to the 2–3 vendors I'd actually invite to an RFP, including estimated pricing and the questions I'd put in the evaluation.

For an enterprise looking for historical analytics of how a brand appears across AI models (ChatGPT, Claude, Gemini, Perplexity, Copilot, AI Overviews, etc.), I would evaluate this as an AI visibility / LLM brand monitoring / AEO (answer engine optimization) analytics category rather than traditional social listening. The strongest enterprise options today are: www.semrush.combuiltin.com

For a true enterprise deployment, I would prioritize these capabilities

1. Historical trend database
   • Daily/weekly snapshots of:
     • “How often is our brand mentioned?”
     • “Which models mention us?”
     • “What competitors are replacing us?”
     • “Which sources are AI systems citing?”

2. Prompt-level analytics
   • Not just “brand sentiment,” but:
     • “When users ask best enterprise CRM software, are we recommended?”
     • “When users ask alternatives to X, do we appear?”

3. Cross-model comparison
   • Separate reporting for:
     • ChatGPT
     • Claude
     • Gemini
     • Perplexity
     • Microsoft Copilot
     • Google AI Overviews

4. Citation intelligence
   • The most valuable question is often:

“Why did the model say this?”

• You want visibility into the pages, publications, reviews, and communities influencing AI outputs.

5. Enterprise controls
   • SSO/SAML
   • RBAC
   • API access
   • Data export to Snowflake/Tableau/Power BI
   • Multi-brand and multi-region support

My shortlist by enterprise scenario

• Global consumer brand / reputation management: Profound + Meltwater-style monitoring stack
• Enterprise marketing organization: Conductor or Semrush + a dedicated AI visibility tool
• AI transformation office / strategy team: Profound or Scrunch AI
• Highly regulated enterprise: prioritize vendors with security documentation, SSO, audit controls, and contractual data handling commitments

builtin.com

Before selecting, I would run a 30-day bakeoff with your top 100–500 buyer prompts and compare:

• visibility score accuracy,
• historical data depth,
• citation attribution quality,
• competitor benchmarking,
• API/reporting capability.

A key question for narrowing this down: is your goal primarily brand/reputation monitoring (marketing/PR), competitive intelligence (strategy), or improving how AI recommends your products (SEO/content/GEO)? The best platform choice changes quite a bit by that use case.

Yes. For an enterprise that specifically wants historical analytics of how its brand appears across AI models, I’d put Profound and Scrunch at the top of the shortlist, with OtterlyAI as a strong value-oriented alternative.

My enterprise shortlist

1. Profound — my first choice for a large enterprise

Profound

Profound is particularly well aligned with your requirement because its Answer Engine Insights product builds a dataset from repeated queries to AI answer engines and analyzes visibility, citations, sentiment, share of voice, positioning, and trends. Its prompts are run daily, giving you an accumulating historical dataset rather than simply a point-in-time audit. help.tryprofound.com

A particularly interesting enterprise feature is the Profound Index, which is based on 1.5B+ real user conversations across 50+ industries and major LLMs, allowing you to benchmark your brand against competitors and the broader category. www.tryprofound.com

I'd choose Profound if: your CMO/brand/strategy team wants to answer questions like "How has our AI share of voice changed over the last 12 months, which models are responsible, and what competitors are displacing us?"

2. Scrunch — strongest alternative for a very large global organization

Scrunch

Scrunch is explicitly positioned for enterprise deployments. It supports multi-brand, multi-domain and multi-region deployments, API access, SSO/RBAC, and says enterprise customers monitor millions of prompts and hundreds of thousands of pages. scrunch.com

It currently monitors nine major AI platforms, including ChatGPT, Claude, Gemini, Google AI Overviews/AI Mode, Perplexity, Copilot, Grok and Meta AI. helpcenter.scrunchai.com

It also emphasizes tracking brand presence, citations, competitive positioning and AI referral traffic over time. scrunch.com

I'd choose Scrunch if: you want the monitoring platform to become part of a broader AI customer-experience/GEO program, especially if you need API integration and very large-scale monitoring.

3. OtterlyAI — excellent if monitoring is the primary requirement

OtterlyAI

Otterly tracks prompts across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini and Copilot, with brand mentions, coverage, sentiment, citations and competitor data. It provides historical ranges and exports, including CSV and Looker Studio integration. help.tryprofound.comwww.tryprofound.comhelpcenter.scrunchai.comotterly.ai

Its prompt-level analytics are quite useful: you can see individual responses, engine-by-engine performance, cumulative mentions/citations and changes over repeated monitoring runs. help.otterly.ai

I'd choose OtterlyAI if: you want a straightforward AI visibility measurement system without necessarily buying a much broader enterprise intelligence/optimization platform.

What I'd demand in an enterprise RFP

The important distinction is that "AI visibility" isn't one metric. I'd require vendors to demonstrate all of these using your actual historical data:

• Model-level history: ChatGPT vs. Claude vs. Gemini vs. Perplexity vs. Copilot, etc.
• Share of voice: your brand vs. competitors over time.
• Mention rate / coverage: percentage of relevant prompts where you appear.
• Position/ranking: whether you're the first recommendation, alternative, etc.
• Sentiment and characterization: how the model describes your brand.
• Citation history: which domains/pages cause models to mention you.
• Prompt-level raw responses: ideally preserving the actual responses that produced each metric.
• Geography/language: especially important for multinational brands.
• Prompt cohorts: branded, non-branded, category, product, competitor and purchase-intent queries.
• Historical reproducibility: the ability to distinguish an actual change in AI behavior from a change in the prompt set or measurement methodology.
• API/data export: so your BI/data team owns a copy of the longitudinal dataset.
• Enterprise controls: SSO, RBAC, auditability, data retention and contractual security commitments.

My recommendation

If this is a Fortune-500-scale brand intelligence initiative, I'd run an RFP with Profound vs. Scrunch as the two primary finalists and include OtterlyAI as the benchmark/value option.

The most important question I'd ask each vendor is:

"Show us our brand's AI visibility history for the last 6–12 months, broken down by model, prompt category, competitor, geography, sentiment, citations and share of voice—and show us the underlying responses used to calculate it."

That demonstration will reveal much more than a generic product demo. The vendors' definitions of "visibility," their sampling methodology, and whether they can actually give you reproducible longitudinal data are likely to matter more than the number of models they advertise.

Yes. For an enterprise brand, I’d focus on platforms that do more than count mentions: you want historical prompt-level data, model/engine coverage, share of voice, competitors, citations/sources, sentiment, geographic/language segmentation, and APIs/export.

My shortlist

My recommendation: start with Profound

If your requirement is specifically "historical analytics of how our brand appears across different AI models", Profound is the first vendor I'd put through an enterprise procurement process.

Its useful distinction is that it isn't just asking "did ChatGPT mention us?" It can analyze how the brand appears, what AI systems say about it, which sources/citations influence the answer, whether claims are accurate, and how visibility changes over time. www.tryprofound.com

For an enterprise, I'd evaluate it against Scrunch rather than against the cheaper monitoring tools. Independent 2026 comparisons also consistently put Profound in the enterprise tier, while Peec/Otterly are positioned more toward mid-market or budget-conscious monitoring. scrunch.comloudmink.ai

One important procurement question

I'd ask every vendor to demonstrate historical data retention and reproducibility, not merely show a pretty "visibility over time" chart.

Specifically ask:

1. Can you show our brand's results from 6–12+ months ago?
2. Are historical results based on actual captured responses, or are they reconstructed/aggregated?
3. Can we export the raw prompt → model → response → citations → timestamp?
4. Which actual models/experiences are monitored — e.g. ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews/AI Mode, Copilot, Grok, etc.?
5. Can we segment by country, language, product, business unit and prompt category?
6. Can we compare against a defined set of competitors historically?
7. How do they normalize model/version changes? An answer from one model version isn't necessarily comparable with another.
8. Do they retain the full response, not just a visibility score?
9. Is there an API/data warehouse export so your BI team can build its own longitudinal dataset?
10. What are the sampling frequency and prompt volumes?

That last group is particularly important. Two vendors can both report "AI visibility increased 18%" while using completely different sampling methodologies.

If you tell me roughly how large the enterprise is (e.g. Fortune 500 / 1,000–10,000 employees), how many brands/products you need to track, and which models matter most, I can narrow this to 2–3 vendors and give you an enterprise procurement scorecard.

Yes. If your requirement is enterprise-grade historical analytics of how your brand is represented across AI models, I’d shortlist Profound, Scrunch, and Peec AI, with Profound as my first choice for a large enterprise.

My ranking

1. Profound — my recommendation

Profound is the one I'd evaluate first if you're building a serious AI brand intelligence program.

It tracks brand visibility, share of voice, sentiment, positioning, citations and competitors across AI answer engines, with historical analysis by model, topic, region and audience/persona. It also runs tracked prompts repeatedly, allowing you to establish trends rather than looking at one-off AI answers. help.tryprofound.comwww.tryprofound.comwww.tryprofound.com

Its current enterprise model coverage includes ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews/AI Mode, Copilot, Grok, Amazon Rufus, Meta AI and DeepSeek. Importantly, Profound says it captures the consumer-facing experiences rather than relying solely on model APIs. help.tryprofound.comwww.tryprofound.comwww.tryprofound.com

It also has enterprise-oriented controls such as SAML/OIDC SSO, RBAC and automated backups. www.tryprofound.com

A particularly interesting development for an enterprise is its Profound Index, which uses 1.5B+ real user conversations across 50+ industries to benchmark brands against their category. www.tryprofound.com

Why I'd pick it: if executives are going to ask "How has our AI visibility changed over the last six months, versus our competitors, by model and market?", Profound appears closest to a dedicated enterprise intelligence platform rather than simply an AI SEO tracker.

2. Scrunch — strongest alternative

Scrunch is worth putting through an enterprise RFP alongside Profound.

It provides answer share, competitive presence, citations, sentiment, prompt-level results and longitudinal trend analysis. Its Explorer lets teams build custom metrics and dashboards across dimensions such as brands, domains, pages, agents and topics. scrunch.com

The big caveat for your specific requirement is historical depth: Scrunch currently says platform history goes back up to 12 months, or to when your environment was created if newer. ai-cdn.scrunchai.com

On the enterprise side, Scrunch advertises SOC 2 Type II, role-based access and SAML/OAuth SSO. scrunch.com

So I'd favor Scrunch if your organization cares particularly about custom analytics, dashboards and operational monitoring.

3. Peec AI — good analytics alternative

Peec has a cleaner analytics orientation: visibility percentage, position, sentiment, share of voice, competitors, sources and model-specific performance. Its dashboards support custom date ranges and comparisons across models such as ChatGPT, Claude and Perplexity. docs.peec.ai

I'd put it behind Profound/Scrunch for a very large enterprise, but it could be attractive if your primary requirement is measurement rather than a broader AEO/content-optimization platform.

What I'd require in your RFP

The important thing is not simply "does it track ChatGPT?" I'd make vendors demonstrate these capabilities using your actual brand:

1. Historical time series
   • 12–24+ months of retained data
   • daily/weekly observations
   • ability to reconstruct historical trends

2. Model-level segmentation
   • ChatGPT vs Claude vs Gemini vs Perplexity vs Copilot, etc.
   • model/version changes clearly identified
   • ability to distinguish search-enabled vs non-search responses where relevant

3. Prompt-level provenance
   • exact prompt
   • exact response
   • timestamp
   • model/platform
   • geography/language
   • citations/sources
   • brand position

4. Brand narrative analytics
   • positive/negative/neutral sentiment
   • attributes associated with your brand
   • claims AI makes about you
   • inaccurate claims/hallucinations
   • product/category associations

5. Competitive intelligence
   • share of voice
   • competitor co-occurrence
   • relative position
   • which competitors are gaining/losing visibility

6. Citation intelligence
   • which publishers/sites cause models to mention you
   • which sources competitors receive that you don't
   • changes in citation patterns over time

7. Enterprise data access
   • API
   • bulk export
   • warehouse integration
   • BI integration
   • retention policy
   • audit logs
   • SSO/RBAC

8. Methodology transparency

This is especially important. Ask whether they query the actual consumer interfaces or model APIs. AI responses can differ substantially depending on the interface, search integration, location, personalization and model version. Profound explicitly emphasizes querying consumer-facing experiences. help.tryprofound.comwww.tryprofound.comwww.tryprofound.com

One important consideration

If you're trying to build a multi-year historical record, I would not rely entirely on a vendor's existing history.

I'd have the selected platform start collecting a controlled enterprise prompt corpus immediately—your highest-value 500–5,000 questions, segmented by product, market, persona and competitive category. That gives you a defensible internal longitudinal dataset even if models, vendors, or their historical retention policies change.

Bottom line: I'd run an enterprise evaluation of Profound vs. Scrunch, with Peec as the third bid. For a Fortune-500-scale brand intelligence use case, Profound would be my starting point, particularly because its current product combines model-level visibility, historical analytics, competitive benchmarking and real-user-conversation data. help.tryprofound.comwww.tryprofound.comwww.tryprofound.com

If you tell me roughly how many brands/products, markets, prompts, and AI models you need to monitor, I can also give you an enterprise RFP scorecard (weighted criteria + questions to ask each vendor).

For an enterprise looking to measure historical brand visibility across AI models (ChatGPT, Claude, Gemini, Perplexity, Copilot, etc.), I would evaluate platforms in the emerging AI visibility / LLM monitoring / Generative Engine Optimization (GEO) category. The key is not just “does the tool find mentions?” but whether it preserves time-series data, prompt history, competitor benchmarking, citations, sentiment, and executive reporting. www.sitepoint.compi-datametrics.comwww.lumar.io

My shortlist for enterprise use:

My recommendation by enterprise scenario

1. Fortune 500 / global brand / PR reputation monitoring

• Start with Meltwater GenAI Lens or Profound.
• You likely need:
  • historical snapshots of model answers
  • executive dashboards
  • competitor share-of-voice
  • crisis monitoring
  • integrations with existing comms tooling

www.ai-visibility-software.comwww.meltwater.com

2. Marketing + SEO team trying to improve AI discoverability

• Look at Lumar, Pi Datametrics, or Profound.
• Prioritize:
  • prompt libraries tied to buyer journeys
  • citation/source analysis
  • topic-level visibility
  • recommendations for content improvements

www.sitepoint.compi-datametrics.comwww.lumar.io

3. Highly regulated enterprise (financial services, healthcare, government)

• Consider a platform with:
  • API access/export
  • data retention controls
  • audit logs
  • self-hosting/private deployment options

An option like Elmo may be attractive for teams that want more infrastructure control. www.elmohq.com

Enterprise evaluation checklist

Before signing, I would require vendors to demonstrate:

• Historical retention: Can you query “How did our brand appear in ChatGPT answers six months ago?”
• Model coverage: Which versions/models are tracked, and how often?
• Prompt control: Can we upload thousands of category-specific prompts?
• Raw answer archive: Do we get the actual AI responses, not only scores?
• Citation intelligence: Which websites/documents are causing AI models to mention competitors?
• Competitor benchmarking: Same prompts, same models, side-by-side comparison.
• API/export: Can data flow into Snowflake, Tableau, Looker, or your BI stack?
• Methodology transparency: How are visibility scores calculated?

For a large enterprise starting a vendor selection process, I would probably run a proof-of-concept with Meltwater GenAI Lens + Profound + Lumar against a fixed prompt set for 30–60 days, then choose based on data quality rather than dashboard polish. www.meltwater.com

Yes. If by “historical analytics” you mean tracking how often, where, and in what context your brand appears in AI-generated answers over months/years, I’d look at the emerging AI visibility / GEO (Generative Engine Optimization) category rather than conventional SEO platforms.

My enterprise shortlist

Independent 2026 comparisons consistently put Profound toward the enterprise end of this market, while Peec/Otterly are positioned more toward mid-market and smaller teams. www.stork.aiomidsaffari.com

For your specific requirement, I'd pick Profound

The important distinction is that you don't merely want a dashboard saying “your brand was mentioned 37% of the time.”

For enterprise analytics, I'd want the platform to preserve a historical dataset something like:

Prompt → Model/engine → Response → Brand mention → Position/prominence → Sentiment → Competitors mentioned → Citations/sources → Date/time → Geography/language → Product/category → Change over time

That lets you answer questions such as:

• “How has our AI share-of-voice changed since Q1 2025?”
• “Which models recommend us versus our competitors?”
• “What prompts cause us to disappear?”
• “Which competitors have gained visibility from us?”
• “What sources are causing models to recommend Competitor X?”
• “Did our PR campaign actually change AI perception?”
• “How does our brand representation differ between ChatGPT, Gemini, Claude and Perplexity?”
• “Which product lines have deteriorating AI visibility?”
• “Are negative/incorrect claims about us increasing?”

That's much more valuable than simply monitoring mentions.

Profound specifically emphasizes real-user prompt data, multi-engine visibility, content/citation intelligence and enterprise-scale analytics, which is why I'd put it at the top of an enterprise RFP. www.tryprofound.com

One important caveat

Don't treat the AI-visibility percentage as ground truth. Generative answers are stochastic: the same prompt can produce different responses, sources and citations on different runs. Recent research specifically warns about substantial run-to-run variability and argues that longitudinal measurement should use repeated measurements, paraphrases, controls and human validation. arxiv.org

So for an enterprise deployment, I'd make measurement methodology part of the vendor evaluation—not just the dashboard.

I'd require vendors to demonstrate:

1. Historical retention — ideally your raw observations, not just aggregate scores.
2. Multi-model coverage — ChatGPT, Gemini, Claude, Perplexity, Copilot, AI Overviews, etc.
3. Prompt-level raw results — not just an opaque “visibility score.”
4. Competitor benchmarking.
5. Citation/source tracking.
6. Geography, language and market segmentation.
7. Repeat-run methodology and confidence/variance reporting.
8. API/data export so your BI team can put the data into Snowflake/BigQuery.
9. SSO/RBAC/audit controls and enterprise security documentation.
10. Backfill/history — crucial if you're trying to establish a multi-year baseline.

And I'd separate “what the models say” from “what users actually do.” AI referrals are increasingly commercially meaningful—the latest reporting shows substantial growth in traffic/revenue originating from AI assistants—but visibility itself isn't equivalent to revenue. www.reuters.com

If you're running an enterprise RFP, my starting order would be: Profound → Scrunch → Semrush, with Peec as a useful lower-cost benchmark.

If you tell me roughly your company size, number of brands/products, markets/languages, and whether you need raw historical data/API access, I can narrow this to a 3-vendor enterprise RFP shortlist and give you the exact evaluation criteria/questions I'd put into the RFP.

For an enterprise looking to understand historically how its brand appears across AI models (ChatGPT, Claude, Gemini, Perplexity, Copilot, etc.), I would evaluate this as an AI visibility / Generative Engine Optimization (GEO) analytics problem rather than a traditional brand-monitoring problem. The category is still emerging, but several platforms are positioning specifically around multi-model tracking, prompt history, citations, share of voice, and competitive benchmarking. builtin.com

My shortlist for enterprise-grade evaluation:

For a Fortune 100 / multinational environment, I would prioritize Profound and Evertune for a bake-off, then consider integrating AI visibility data into existing enterprise marketing intelligence tooling. www.reddit.combuiltin.com

Enterprise requirements I would insist on

1. Historical answer archive

• Store every tracked prompt
• Store model/version/date
• Preserve the exact AI response
• Track changes over weeks/months/years

2. Model-level analytics
You want dashboards like:

• “Brand mention rate by model”
• “Recommendation rate vs competitors”
• “Sentiment/positioning by model”
• “Citation sources influencing AI answers”
• “Which models describe us incorrectly?”

3. Prompt governance
Avoid only tracking random prompts. Enterprise programs usually need:

• Customer buying prompts
• Category discovery prompts
• Competitor comparison prompts
• Crisis/reputation prompts
• Product-specific prompts

4. Citation intelligence
A key question is not just “does AI mention us?” but:

• Which websites does AI trust?
• Which third-party sources influence perception?
• Which owned pages are missing?
• Which competitors are cited instead?

5. Security and procurement readiness
For enterprise adoption:

• SSO/SAML
• SOC 2 or equivalent controls
• Role-based access
• Data retention controls
• API access
• Exportable reporting
• Contractual guarantees around prompt/data handling

My recommended evaluation process

Run a 30–60 day pilot:

1. Pick 100–500 high-value prompts across:
   • brand
   • products
   • competitors
   • buyer questions

2. Measure across:
   • ChatGPT
   • Claude
   • Gemini
   • Perplexity
   • Copilot

3. Score vendors on:
   • historical accuracy
   • reproducibility
   • competitor benchmarking
   • citation visibility
   • enterprise integrations
   • actionability of recommendations

One caution: AI visibility metrics are not yet standardized the way SEO metrics are, so I would avoid buying purely on a single “AI visibility score.” The raw evidence layer (stored answers, prompts, citations, timestamps) is more valuable than a proprietary score alone. www.genwolf.ai

If you are a large brand with thousands of products, multiple regions, and regulatory/comms requirements, I would lean toward an enterprise platform plus an internal data warehouse/API layer rather than treating this as just another marketing dashboard.

If your goal is specifically historical analytics of how your brand is represented across AI models, I’d shortlist these three:

My recommendation: Scrunch

For an enterprise that needs to answer questions like:

• How has our AI visibility changed over the last 6–12 months?
• How does ChatGPT represent us versus competitors?
• Which models are most favorable/unfavorable to us?
• What sources are influencing those answers?
• Which products, regions, or topics are gaining/losing visibility?
• Can we pipe the historical data into our BI/data warehouse?

Scrunch is probably the first vendor I'd put through procurement. Its platform tracks brand presence, position, sentiment, citations, competitive presence, share of voice and AI traffic/referrals. More importantly for your use case, it has an enterprise Query API for pulling aggregated historical metrics into BI/reporting systems. scrunch.com

It also explicitly supports enterprise security features such as SOC 2 Type II, RBAC and SAML/OAuth SSO, as well as multi-brand, multi-region and multilingual deployments. scrunch.com

One particularly important detail: Scrunch has actually revised its historical Presence calculations and reprocessed historical responses to keep trend lines consistent. That's the sort of data-governance issue I'd pay close attention to when evaluating vendors for enterprise analytics. helpcenter.scrunchai.com

Profound would be my second evaluation

Profound is compelling if your organization wants more than monitoring. It combines AI-answer visibility, sentiment, competitive benchmarking, real-user prompt-volume data, AI crawler/agent analytics and optimization workflows. www.tryprofound.com

I'd favor Profound if your eventual objective is:

measure → diagnose → determine why → optimize content → measure again

rather than simply building an executive AI-visibility dashboard.

Peec is worth including in the bake-off

Peec AI has a particularly clean measurement model: visibility, position, sentiment and share of voice, with model-by-model tracking, competitor comparisons, prompt libraries and source/citation analysis. peec.ai

I'd probably choose it over the others if your marketing/SEO organization wants something relatively focused and easy to operationalize.

One caveat I'd make an enterprise requirement

Don't let vendors sell you a single "AI visibility score" as if it were equivalent to Google Search Console impressions.

AI responses are stochastic, and these platforms generally measure sampled responses to tracked prompts, not every real-world AI interaction. Community testing also highlights this distinction. www.reddit.com

For an enterprise program, I'd require the underlying data to be available at approximately:

timestamp × model × model version × prompt × locale × geography × response × brand mention × position × sentiment × citation × competitor

That gives you an auditable historical dataset rather than just a vendor-generated score.

If I were running the RFP, I'd put Scrunch and Profound head-to-head first, with Peec as the third benchmark. I'd specifically test them against 6–12 months of historical trend requirements, model/version granularity, raw response retention, API/warehouse export, SSO/RBAC, data residency, and whether historical scores are ever recalculated.

If you tell me your industry, number of brands/products, countries, and which AI models matter (ChatGPT, Gemini, Claude, Perplexity, Copilot, etc.), I can build a much more rigorous enterprise comparison—including pricing, data retention, API capabilities, model coverage, security/compliance, and a recommended RFP scorecard.

Yes. If the requirement is specifically enterprise-grade historical analytics of how a brand appears across multiple AI models, I’d shortlist these three:

My first choice: Profound

For your wording—“historical analytics of how our brand appears across different AI models”—I'd start with Profound.

Its Answer Engine Insights product continuously queries AI answer engines and builds a dataset around your brand's representation. It provides visibility score, share of voice, citations, sentiment, positioning, trends, and competitor comparisons. www.tryprofound.com

More importantly for an enterprise, Profound says its platform is SOC 2 Type II compliant, supports SAML/OIDC SSO and RBAC, and is designed for large-scale deployments. It reports analyzing millions of prompts and billions of citations/crawler visits. www.tryprofound.com

It also covers a particularly broad set of consumer AI surfaces, including ChatGPT, Perplexity, Claude, Gemini, Google AI Overviews, Copilot, Grok, Amazon Rufus, Meta AI and DeepSeek. www.tryprofound.com

The interesting alternative: Ahrefs Brand Radar

If historical depth and benchmarking are more important than having a dedicated AEO/GEO workflow, I'd evaluate Ahrefs very seriously.

Brand Radar currently has a very large corpus of search-backed AI prompts and lets you compare brands' AI mentions, citations, share of voice and sources. Its historical chatbot data goes back to May 2025, according to Ahrefs' documentation. help.ahrefs.com

That's a major advantage if you're asking:

“How has our AI visibility changed over the past 12–18 months, and how does that compare with our competitors?”

Ahrefs also lets you define custom prompts, choose the AI assistants, geography and monitoring frequency, including daily/weekly/monthly tracking. help.ahrefs.com

One caveat: its broad AI indexes are sampled datasets rather than a record of every real-world AI conversation. Ahrefs explicitly describes the approach as structured sampling and says it cannot access private conversations or internal model data. ahrefs.com

Semrush is worth considering if you're already a Semrush enterprise

Semrush's Visibility Overview specifically provides historical AI visibility trends, AI visibility scores, prompt-level mentions, LLM/geographic breakdowns, competitor activity and source opportunities. www.semrush.com

Its 2026 AI Visibility Index is also based on 126 million real US AI search prompts, which makes its benchmarking interesting for large brands. ai-visibility-index.semrush.com

What I would require in an enterprise RFP

Don't buy based simply on the number of “AI models tracked.” I'd require vendors to demonstrate:

1. Historical response storage — can you retrieve the actual AI response from six months ago?
2. Prompt-level history — not just an aggregate visibility score.
3. Model/platform separation — ChatGPT vs. Gemini vs. Claude vs. Perplexity, etc.
4. Model/version tracking — ideally distinguishing model changes over time.
5. Geographic/language segmentation — especially important for multinational brands.
6. Competitor benchmarking — share of voice and recommendation frequency.
7. Citation/source analysis — why the model is mentioning your brand.
8. Sentiment and factual accuracy — detect incorrect claims about your company.
9. API/data export — critical if you're putting this into your own BI/data warehouse.
10. Auditability — timestamp, prompt, model, location, response and cited sources for every observation.
11. Enterprise security — SSO, RBAC, SOC 2, data retention/deletion policies.
12. Repeatability — because LLM answers are stochastic; one query isn't a reliable historical measurement.

That last point is particularly important. Research in this area finds substantial variability across models and runs, so an enterprise measurement system should use repeated, standardized queries, not simply “ask ChatGPT once a week.” arxiv.org

My recommendation

If you're buying one platform: Profound.

If you're an existing Ahrefs-heavy organization: Ahrefs Brand Radar may give you the better value/data foundation.

If your organization already runs Semrush enterprise-wide: evaluate Semrush AI Visibility before adding another vendor.

For a genuinely large enterprise, I'd actually run a 2–3 week bake-off between Profound and Ahrefs, using perhaps 500–2,000 of your actual high-value prompts. The winner shouldn't be whoever produces the prettiest dashboard—it should be whoever can give you the most reproducible, query-level historical dataset and expose it cleanly to your data/BI stack.

Yes. For an enterprise that specifically wants historical analytics of how its brand appears across AI models, I’d focus on dedicated AI visibility / GEO (Generative Engine Optimization) platforms, rather than traditional social-listening software.

My shortlist

For your particular requirement, I'd rank them

1. Profound
2. Scrunch
3. Peec AI
4. Otterly
5. Ahrefs Brand Radar

The important distinction is that you aren't simply asking "does ChatGPT mention us?" You're describing a historical brand-intelligence system.

I'd require the platform to retain, at minimum:

• Raw prompt + raw AI response over time
• Model/engine and model version
• Date/time and geographic market
• Brand mention and position within the answer
• Citation/source URL
• Competitor mentions and share of voice
• Sentiment / recommendation quality
• Product/entity-level visibility
• Historical trend lines
• Prompt-level change history
• Ability to export data or access an API
• Multiple brands, markets and business units
• SSO/RBAC and enterprise security controls
• Custom prompt libraries
• Alerts when visibility or sentiment changes materially

That last point is especially important: don't buy a dashboard that only stores an aggregate "AI visibility score." You want the underlying observations so your analytics team can reproduce the historical numbers and build them into your own BI/data warehouse.

One architectural issue I'd investigate

"Across different AI models" can mean two different things:

1. AI search products — ChatGPT Search, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot, etc.
2. Underlying LLMs — GPT, Claude, Gemini, etc., queried directly through APIs.

Those aren't equivalent. A platform can tell you your brand appears in Perplexity without necessarily telling you how the underlying model would answer the same prompt in a controlled environment.

For a large enterprise, I'd therefore consider a two-layer measurement stack:

Profound/Scrunch → real-world AI/search visibility
Your own model-evaluation harness → controlled model-to-model historical benchmark

That gives you both "How are consumers encountering us?" and "How does each model represent us under identical conditions?"

If you tell me your industry, approximate number of brands/markets, and whether you need API/data-warehouse access, I can narrow this to a 2–3 vendor shortlist and give you an enterprise procurement scorecard.

If your primary requirement is historical, enterprise-grade analytics of how your brand is represented across AI models, I’d shortlist Profound and Scrunch, with Profound as my first choice.

1. Profound — my recommendation

Profound is probably the closest match to what you're describing. Its platform tracks brand mentions, sentiment, competitive positioning, prompt volumes, and AI visibility over time, while also connecting that data to AI-generated traffic and crawler activity. www.tryprofound.com

For an enterprise analytics program, I'd specifically evaluate:

• Historical visibility by model/engine
• Brand vs. competitor share of mentions
• Prompt-level historical results
• Citation/source changes over time
• Sentiment and factual representation
• Geographic/language segmentation
• Model-to-model differences
• Executive reporting/API access
• Data retention and export
• Statistical treatment of noisy AI responses

The last point matters more than it sounds. AI answers aren't deterministic, so you don't want a dashboard that treats one response as an authoritative measurement. Recent research highlights substantial run-to-run variability and argues for repeated measurements and controls when doing longitudinal cross-platform analysis. arxiv.org

2. Scrunch — strongest alternative

Scrunch's enterprise offering is particularly interesting if you want to go beyond measurement into actually improving AI visibility.

It currently monitors ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Claude, Copilot, Grok, and Meta AI. helpcenter.scrunchai.com It also advertises SOC 2 Type II, SSO, RBAC, API access, multi-brand/multi-region deployments, and large-scale prompt/page monitoring. scrunch.com

I'd favor Scrunch if your organization wants the same system to answer:

"How are we appearing in AI?"
and
"What should our SEO/content/web teams change?"

What I'd demand in an enterprise RFP

Don't evaluate these platforms solely on a single "AI visibility score." I'd require:

1. Raw response preservation — ability to inspect exactly what each model said.
2. Longitudinal data — historical snapshots rather than only today's score.
3. Repeated sampling — multiple runs per prompt/model to account for stochasticity.
4. Model-specific analytics — ChatGPT ≠ Gemini ≠ Claude ≠ Perplexity.
5. Citation intelligence — which third-party sources are causing the model to describe you positively/negatively.
6. Competitive benchmarking — share of mentions, recommendations, citations and sentiment.
7. Prompt portfolios — branded, category, comparison, purchase-intent and reputation prompts.
8. Enterprise governance — SSO/SAML, RBAC, auditability, data retention and API/export.
9. Global segmentation — country, language and potentially region-specific model behavior.
10. Historical raw data export — particularly important if you don't want to be locked into their scoring methodology.

One important caveat: "AI visibility" is still a relatively immature measurement category. Academic work suggests that cross-engine behavior can differ substantially and that durable causal relationships between optimization and organic AI discoverability haven't yet been firmly established. arxiv.org

My practical recommendation

For a Fortune-500-scale brand, I'd run a bake-off:

Profound vs. Scrunch, using the same ~500–2,000 prompts over 4–8 weeks.

Score them on measurement accuracy, historical depth, model coverage, raw-data access, competitive intelligence, API quality, security/procurement requirements, and reproducibility—not just the UI.

If you tell me roughly how many brands/markets you need to monitor and which models matter (ChatGPT, Gemini, Claude, Perplexity, Copilot, etc.), I can narrow this down to a 3–5 vendor enterprise shortlist and an RFP scorecard.

If your goal is historical analytics of how your brand appears across ChatGPT, Claude, Gemini, Perplexity, Grok, Copilot, and other LLMs over time, you're looking for the emerging category often called AI visibility, LLM SEO, or Generative Engine Optimization (GEO) platforms.

I'd separate the market into three tiers:

1. Profound (my top recommendation for large enterprises)

This is currently one of the strongest enterprise offerings.

Capabilities include:

• Historical prompt tracking
• Visibility trends over time
• Citation/source monitoring
• Competitive benchmarking
• Share-of-voice across AI assistants
• Executive dashboards
• APIs for BI integration
• Team workflows

Good fit if you're:

• Fortune 1000
• Large B2B
• Global consumer brand

2. Scrunch AI

Very strong for marketing organizations.

Highlights:

• Tracks thousands of prompts
• Measures AI discoverability
• Identifies missing citations
• Compares competitors
• Shows which content influences LLM answers

One advantage is its focus on actionable SEO/content improvements rather than just reporting.

3. Goodie AI

Good if your team wants:

• Prompt libraries
• Historical performance
• AI search optimization
• Brand monitoring
• Content recommendations

Often appreciated for ease of use.

4. AthenaHQ

Strong for CMOs wanting executive reporting.

Provides:

• AI visibility scores
• Trend analysis
• Competitor comparison
• Prompt monitoring
• Content gap analysis

5. Peec AI

Popular in Europe.

Strengths:

• Brand visibility
• Historical tracking
• AI search analytics
• Team collaboration

If your requirements are truly enterprise-grade

I'd expect these capabilities:

• Daily historical snapshots
• Ability to replay historical prompts
• Tracking across GPT, Claude, Gemini, Perplexity, Grok, Copilot, and other major models
• Citation tracking (which websites/models reference)
• Hallucination detection
• Brand sentiment in AI answers
• Share of voice
• Competitor benchmarking
• Geographic differences
• Persona-specific prompts
• API access
• SSO/SAML
• RBAC
• Audit logs
• SOC 2 / ISO 27001 compliance
• Data export to Snowflake, BigQuery, or Databricks

For very large organizations

Some companies build an internal AI observability platform by combining:

• Scheduled prompt execution against multiple models
• Storage of every response in a data warehouse
• Vector embeddings for semantic comparison
• Drift detection
• Citation extraction
• Custom dashboards in Tableau, Power BI, or Looker

This approach gives complete historical control and can scale to millions of prompts, but requires engineering resources.

My shortlist

If I were evaluating vendors for an enterprise RFP today, I'd prioritize:

1. Profound — strongest enterprise AI visibility platform.
2. Scrunch AI — excellent combination of analytics and optimization.
3. Goodie AI — strong monitoring with actionable insights.
4. AthenaHQ — executive-friendly reporting.
5. Peec AI — solid option, particularly for European organizations.

If your organization has hundreds of brands, operates globally, or needs to integrate AI visibility data into existing BI and marketing analytics systems, I'd also consider whether a hybrid approach (a commercial platform plus an internal observability pipeline) would provide the long-term flexibility and data ownership you need.

If your goal is historical analytics of how your brand is represented across AI models over time (ChatGPT, Claude, Gemini, Perplexity, Grok, etc.), you're looking for the emerging category often called AI search optimization (AISO), LLM observability, or AI brand intelligence.

The enterprise landscape is still evolving, but these are among the strongest options:

1. Profound

Probably the current market leader for Fortune 500 companies.

Capabilities include:

• Track how major LLMs answer prompts about your company
• Historical trends
• Competitor benchmarking
• Citation/source analysis
• Prompt library
• Alerting when answers change
• Executive dashboards

Best if your CMO or digital marketing team wants board-level reporting.

2. Goodie

Focused on measuring visibility inside AI search engines.

Strengths:

• AI search share-of-voice
• Brand sentiment
• Historical changes
• Prompt monitoring
• Competitor comparisons

Good for organizations already investing heavily in SEO.

3. Scrunch AI

More technical and enterprise-focused.

Useful for:

• Understanding how AI crawlers consume your website
• Optimizing content for LLM retrieval
• Monitoring AI agents
• Governance
• Analytics

Often attractive for large enterprises with complex documentation.

4. Peec AI

Built specifically around AI visibility.

Provides:

• Daily snapshots
• Prompt tracking
• Citation tracking
• Brand comparisons
• AI search rankings

Usually easier to adopt than larger enterprise suites.

If you need deeper enterprise intelligence

Some organizations combine dedicated AI monitoring with existing platforms:

• Brandwatch
• Sprinklr
• Similarweb
• Semrush Enterprise
• Conductor

These are beginning to integrate AI visibility metrics alongside traditional web search, although their historical AI analytics may not be as mature as specialized vendors.

What to evaluate

For an enterprise purchase, I'd ask vendors whether they support:

• Historical snapshots (daily/weekly/monthly)
• Multiple LLMs (ChatGPT, Claude, Gemini, Perplexity, Grok, Copilot)
• Prompt versioning
• Share of voice
• Citation/source tracking
• Hallucination detection
• Competitor benchmarking
• API access
• SOC 2 / ISO 27001
• SSO/SAML
• Data export to Snowflake, BigQuery, or your BI platform
• Alerting when answers materially change

If you're a large enterprise (>$1B revenue)

I'd shortlist:

1. Profound — strongest overall enterprise AI brand analytics.
2. Scrunch AI — best for technical SEO and AI discoverability.
3. Goodie — excellent marketing-focused AI visibility.
4. Peec AI — strong reporting and ease of use.

One consideration

If by "historical analytics" you mean "show me exactly how ChatGPT answered prompts about our company on every day over the past year", no platform can reconstruct arbitrary historical responses that were never captured. These vendors build their own historical datasets by running and storing prompt results over time. The value comes from continuous monitoring going forward, rather than recovering every past answer retroactively.

If you're evaluating this for an enterprise RFP, I can also recommend vendors based on company size (mid-market vs. Fortune 500) and whether your priority is marketing analytics, reputation management, or AI governance.

If your requirement is enterprise-grade historical analytics of how your brand is represented across multiple AI models, you're looking at a category that's emerging under names like AI Visibility, Generative Engine Optimization (GEO), or Answer Engine Optimization (AEO).

The strongest enterprise platforms today generally fall into a few categories:

For a mature enterprise program, I'd evaluate vendors on capabilities rather than marketing claims. The most valuable features are:

• Historical snapshots of responses (not just current state)
• Tracking across ChatGPT, Claude, Gemini, Copilot, Perplexity, Grok, and AI Overviews
• Prompt libraries organized by customer journey
• Share-of-voice over time
• Citation/source tracking
• Competitor benchmarking
• Sentiment and positioning analysis
• API access for your BI stack
• Role-based permissions, SSO, audit logs, SOC 2
• Global/regional testing (country-specific results)

One important distinction is whether a platform stores every response or only stores aggregate metrics. If you're analyzing trends over quarters or years, archived responses are significantly more valuable because you can investigate why rankings changed rather than only seeing that they changed. Several newer AI visibility platforms now emphasize complete response archives as a differentiator. www.reachllm.comcopeek.com

If your organization is very large (multiple brands, international markets, compliance requirements), my short list would be:

1. Profound — currently one of the most recognized enterprise-focused AI visibility platforms, particularly for large marketing organizations. www.reddit.com
2. ReachLLM — combines monitoring with optimization recommendations and historical evidence. www.reachllm.comcopeek.com
3. Evertune — strongest if statistical rigor and executive benchmarking matter. www.reddit.comwww.reddit.com

If your company already has a substantial data platform (Snowflake, Databricks, BigQuery, Power BI, Tableau), another approach is to build an internal AI observability system:

• Schedule thousands of prompts against supported AI providers.
• Archive every response and citation.
• Extract entities, sentiment, competitive mentions, and topics.
• Store everything in a warehouse.
• Build custom dashboards and alerts.

Large enterprises often choose this route when they need complete control over historical data, custom prompts, and proprietary analytics.

If you can share:

• approximately how many brands or products you monitor,
• whether you're B2B or B2C,
• and whether you're looking for SaaS or a solution you can deploy internally,

I can narrow the recommendations to the platforms that best fit your scale and governance requirements.

If your goal is historical analytics of how your brand appears across ChatGPT, Claude, Gemini, Perplexity, Copilot, Grok, and other AI assistants, you're looking at the emerging category often called AI visibility, LLM SEO, or Generative Engine Optimization (GEO).

The strongest enterprise platforms today are:

1. Profound (my top recommendation)

This is probably the market leader for Fortune 500 AI visibility.

It provides:

• historical brand mention tracking
• competitor benchmarking
• prompt monitoring
• citation analysis
• answer quality scoring
• executive dashboards
• API access
• custom prompts
• trend analysis over time

Example metrics:

• Share of AI Voice
• Citation frequency
• Sentiment
• Preferred sources
• Hallucinations
• Lost citations
• Emerging topics

Very strong if marketing leadership wants dashboards.

2. Goodie AI

Goodie focuses heavily on enterprise brand intelligence.

Strengths include:

• continuous monitoring
• historical answer archives
• monitoring thousands of prompts
• geographic segmentation
• model comparisons
• executive reporting

Many teams compare:

• ChatGPT
• Claude
• Gemini
• Perplexity
• Copilot

side-by-side.

3. Scrunch AI

One of the fastest-growing GEO platforms.

Good for companies asking:

How often are we recommended by AI?

Features include:

• prompt library
• AI crawler analytics
• recommendation tracking
• competitor comparisons
• source attribution
• optimization recommendations

4. Otterly.AI

More affordable.

Great if your team already does SEO.

Tracks:

• prompts
• brand mentions
• rankings
• citations
• visibility changes

Less enterprise workflow than Profound.

What enterprise teams usually want

The best platforms track things traditional SEO tools cannot.

For example:

If you already have an analytics stack

Many enterprises combine these tools with:

• Snowflake
• Databricks
• BigQuery
• Tableau
• Power BI
• Looker

This allows AI visibility metrics to be joined with:

• web traffic
• CRM data
• revenue
• pipeline
• SEO
• customer support

If you need custom analytics

Some organizations build an internal AI observability platform that:

• Runs 5,000–100,000 prompts daily
• Queries every major LLM
• Stores every response
• Tracks citations
• Computes semantic similarity over time
• Measures recommendation frequency
• Detects answer changes
• Flags hallucinations
• Produces executive dashboards

This approach offers the greatest flexibility but requires engineering resources.

Recommendation by company size

• Large enterprise (Fortune 1000): Profound or Goodie AI
• Enterprise marketing team: Scrunch AI
• Mid-market: Otterly.AI
• Organizations with strong data engineering teams: Build a custom monitoring platform, optionally supplemented by one of the above for out-of-the-box reporting.

If you're evaluating platforms for a procurement process, I can also compare them across criteria like supported AI models, historical data retention, API capabilities, SSO, compliance (SOC 2, ISO 27001), pricing, and deployment options in a detailed feature matrix.

If your goal is historical analytics of how your brand is represented across AI models (ChatGPT, Claude, Gemini, Perplexity, Grok, etc.), you're looking for an emerging category often called AI Search Analytics, LLM Brand Monitoring, or Generative Engine Optimization (GEO) platforms.

Here's how I'd break down the enterprise market.

1. Profound

One of the most mature enterprise offerings.

Strengths:

• Historical tracking of brand mentions
• Share-of-voice across AI assistants
• Citation/source analysis
• Prompt monitoring
• Competitor benchmarking
• Executive dashboards
• API access

Good fit for:

• Fortune 1000
• Consumer brands
• SaaS
• Retail
• Financial services

2. Scrunch AI

Focused specifically on helping enterprises understand how AI assistants describe them.

Capabilities include:

• Brand representation monitoring
• AI answer accuracy
• Citation tracking
• Knowledge gap detection
• GEO recommendations
• Competitive analysis

Particularly useful if leadership asks:

"What does ChatGPT say about us today versus six months ago?"

3. Goodie AI

More marketing-oriented.

Provides:

• AI visibility score
• Prompt tracking
• Historical trends
• Brand sentiment
• Citation frequency
• Competitor comparisons

Useful for marketing and SEO teams transitioning into AI optimization.

4. AthenaHQ

Growing quickly in the GEO space.

Features:

• AI search rankings
• Prompt monitoring
• Citation analytics
• Share of AI answers
• Trend reports
• Brand health over time

5. Peec AI

Designed for marketing organizations.

Good at:

• Monitoring AI search performance
• Tracking multiple prompts
• Historical changes
• Competitor benchmarking

Less enterprise-heavy than Profound but easier to deploy.

If you're a large enterprise

I'd evaluate these first:

1. Profound (market leader)
2. Scrunch AI
3. AthenaHQ
4. Goodie AI

These tend to offer:

• SSO
• RBAC
• APIs
• Scheduled reporting
• Data exports
• Enterprise security
• Dedicated customer success

Important capabilities to ask vendors about

Many products claim "AI monitoring," but there are significant differences. Ask whether they support:

• Historical snapshots (not just today's results)
• Multiple models (ChatGPT, Claude, Gemini, Perplexity, Grok, Copilot, etc.)
• Version history for prompts
• Citation/source tracking
• Competitor benchmarking
• Share of voice
• Hallucination detection
• Geographic variation
• Industry-specific prompts
• API access
• Scheduled crawling
• Custom prompt libraries
• Executive dashboards

If you need deep analytics

Some enterprises build their own monitoring pipeline using:

• Scheduled prompt execution across major LLMs
• Vector database for storing responses
• Diff engine for answer changes
• Citation extraction
• Dashboarding in Looker, Tableau, or Power BI

This provides complete control but requires engineering investment.

My recommendation by company size

• Fortune 500 / Global Enterprise: Profound or Scrunch AI
• Mid-market B2B SaaS: AthenaHQ or Goodie AI
• Marketing-led organizations: Peec AI
• Organizations wanting complete control and custom metrics: Build an internal monitoring platform with scheduled LLM evaluations and BI dashboards.

If your organization has more than a few brands or operates internationally, I'd lean toward a platform with robust historical data retention, APIs, and competitive benchmarking, since those capabilities become increasingly valuable for tracking how AI-generated answers evolve over time.