ChatGPT answers, unedited

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.