Positive mentions of Peec AI

What you’re looking for is generally called AI visibility / GEO (Generative Engine Optimization) tracking.

These tools repeatedly run a set of buyer-relevant prompts against ChatGPT and other AI engines, then record whether your company appears, where it appears in the answer, which competitors appear, and which sources/citations are being used.

Tools worth looking at

• Profound — geared toward larger marketing/SEO teams; tracks ChatGPT visibility, citations, prompts, competitors, and answer-engine performance. Lectern Austin Heaton
• Peec AI — focused on AI-search analytics and competitor comparison; useful if you want a relatively straightforward visibility dashboard. Lectern The Rank Masters
• Otterly.AI — more accessible for ongoing prompt monitoring and tracking brand mentions across several AI engines. Refine AI Marqeable
• Ahrefs Brand Radar / Semrush AI visibility tools — worth considering if your team already uses either SEO platform. JetFuel Agency Austin Heaton

One important distinction

If by “where exactly it ranks” you mean:

“For the prompt best accounting software for startups, am I #1, #3, or not mentioned at all?”

then you want a tracker that captures the actual answer and mention position/prominence, not merely a “visibility score.” Some tools specifically measure whether a brand is near the top of an AI answer versus buried later in the response. The Rank Masters

Also, ChatGPT doesn't have a fixed universal ranking like Google's traditional 10 blue links. ChatGPT Search selects and synthesizes sources dynamically, and OpenAI says placement isn't guaranteed. OpenAI OpenAI Help Center

If your goal is specifically “show me every prompt where my company appears, its position in the answer, the competitors above/below it, and the cited sources,” I'd focus the evaluation on Profound, Peec, and Otterly rather than a conventional SEO rank tracker.

If you give me your company name + industry, I can also tell you exactly what prompt set I'd use to measure its ChatGPT visibility and ranking.

What you’re looking for is usually called AI visibility / GEO tracking: tools that repeatedly run the same prompts through ChatGPT and record whether your company appears, where it appears in the answer/list, which competitors appear, and which sources ChatGPT cites.

A few current options:

• otterly.ai — probably the easiest starting point. It tracks ChatGPT prompts, brand mentions, competitor share of voice, citations, and average rank/order of mentions. It also lets you see the underlying answers. Otterly Otterly
• tryprofound.com — more enterprise-oriented, with deeper AI-search analytics and prompt/answer monitoring. Current published comparisons put its entry plan around $99/month. Lectern
• peec.ai — focused on AI-search visibility and competitive share of voice; a good option if you're primarily interested in marketing analytics and competitor comparisons. Conbersa

One important distinction

Don't just track "does ChatGPT mention us?"

For example, you want a dashboard that can tell you:

That position/order information is particularly important, because simply being mentioned near the bottom of a 10-company answer is very different from being the first recommendation. Tools in this category increasingly track mention prominence/position rather than merely counting mentions. The Rank Masters

What I'd use

If you're a normal marketing/SEO team, I'd start with OtterlyAI because it explicitly tracks the answer, mention order/rank, citations, competitors, and share of voice across ChatGPT and other AI engines. Otterly

If you're an enterprise with a large prompt set and serious reporting requirements, I'd investigate Profound alongside it.

One caveat: there isn't a single permanent "ChatGPT ranking." Answers can vary with the exact prompt, model, location, personalization, conversation context, and time. A good tracker therefore measures a defined set of prompts repeatedly, rather than claiming that your company has one universal ChatGPT rank. Otterly explicitly notes this variability between manual ChatGPT searches and its standardized monitoring. Otterly

If you tell me your company + industry + roughly how many prompts you want to track, I can also lay out a concrete tracking setup (e.g. 100 prompts, competitors, KPIs, and how to calculate your true "ChatGPT rank").

If you’re managing multiple client brands across different industries, I’d treat this as an AI-search measurement platform problem, not simply a “ChatGPT rank tracker.”

My recommendation

Use an agency-oriented AI visibility platform as the core system, with a standardized prompt/SOV methodology across every client.

A good shortlist is:

• tryprofound.com — strongest fit if you need enterprise-grade depth, large-scale prompt intelligence, and cross-industry analysis. Its current Index is built from 1.9B+ real-user conversations across 50+ industries, which is particularly relevant when you're benchmarking clients against broader category behavior. Profound
• peec.ai — worth evaluating specifically for an agency/multi-client setup; current industry comparisons identify it as an agency-oriented option with multi-client tracking. AI Visibility Software
• semrush.com — attractive if your agency already lives in Semrush and wants AI visibility integrated with traditional SEO, content and competitive data. Semrush
• getmentioned.co — interesting if you want a relatively straightforward multi-brand monitoring/reporting layer. It supports prompt-level evidence, daily monitoring, geographic segmentation and an agency/enterprise tier with multi-brand management and white-label reporting. GetMentioned GetMentioned

If I were designing the stack for an agency today, I'd shortlist Profound + Peec AI first, then Semrush if you already have substantial Semrush infrastructure.

More important than the vendor: define SOV correctly

I would create a standardized measurement model like this:

AI Share of Voice =

Brand mentions ÷ total competitor-brand mentions

across a fixed set of commercially relevant prompts.

But don't stop at that number. For every client, capture:

This distinction matters because a brand can have high mention volume while rarely being the first recommendation, for example.

Build a prompt library for every client

I'd use a common taxonomy rather than letting each account manager invent prompts.

For each brand, create roughly 50–200 prompts, depending on account size:

1. Category discovery

• "What are the best [category] companies?"
• "Best [product/service] for [audience]"
• "Who are the leading [category] providers?"

2. Commercial comparison

• "[Brand] vs [competitor]"
• "Alternatives to [competitor]"
• "Compare the leading [category] options"

3. Problem/need based

• "How do I solve [problem]?"
• "What should I look for when buying [category]?"
• "What [product/service] is best for [use case]?"

4. Audience-specific

• "Best [category] for small businesses"
• "Best [category] for enterprise"
• "Best [category] for [industry/persona]"

5. Geographic

• "Best [category] in the US"
• "Best [category] in [city/country]"
• "[category] providers serving [market]"

6. Brand-specific

• "What is [Brand] known for?"
• "Is [Brand] worth considering?"
• "Who competes with [Brand]?"

The important thing is to freeze the core prompt set so that month-over-month SOV is actually comparable.

Separate "measurement prompts" from "discovery prompts"

This is a big one.

I'd maintain two datasets:

Core SOV dataset

• Fixed prompts
• Same markets
• Same languages
• Same cadence
• Used for executive reporting

Exploration dataset

• Newly discovered questions
• Search/query expansion
• Competitor prompts
• Emerging customer language
• Used to discover opportunities

Otherwise, changing the prompts every month can manufacture changes in SOV.

Don't monitor ChatGPT alone

Even if the client's immediate question is "What's our ChatGPT share of voice?", I'd instrument at least:

ChatGPT + Google AI Overviews/AI Mode + Perplexity + Gemini

and potentially Claude/Copilot/Grok depending on the audience.

The current AI-monitoring market is already moving toward multi-engine measurement rather than ChatGPT-only tracking. TechnologyAdvice cloro

I'd still report ChatGPT separately, because it is a meaningful channel in its own right.

The agency dashboard I'd build

At the top:

Client: Acme Corp
Industry: B2B SaaS
Market: US

AI SOV: 18.4% ↑ 3.1pp
Mention rate: 42% ↑ 6pp
Recommendation rate: 27% ↑ 4pp
Citation rate: 19% ↓ 2pp

Then:

Visibility by engine

Then:

Competitor movement

Brand A → 21%
Brand B → 18%
Brand C → 14%
Client → 18%

And finally the useful part:

Why did visibility change?

For example:

• 12 new prompts began mentioning the client
• Competitor X gained visibility on "best [category]" queries
• Client appeared in 8 newly discovered publications cited by AI engines
• Client disappeared from 4 comparison prompts
• ChatGPT increasingly cited Publication Y

That turns an interesting metric into something a client can actually act on.

For an agency, I would also require raw-answer access

This is a critical buying criterion.

Don't buy a platform that only gives you:

"Brand visibility: 63"

You want to be able to drill into:

Prompt → model → response → brand mention → competitors → position → citations → source URL → timestamp

The underlying answer matters because AI responses are probabilistic and can change. Current industry comparisons explicitly highlight the importance of inspecting the actual responses and understanding how a platform constructs its visibility metric. Beamtrace

My suggested operating model

I'd structure the agency like this:

Data layer
→ AI visibility platform

Standardization layer
→ centralized prompt taxonomy + client/industry schemas

Analysis layer
→ SOV, mention rate, position, citations, competitors

Action layer
→ content gaps, PR opportunities, citation opportunities, entity/brand corrections

Reporting layer
→ Looker Studio / Power BI / Tableau or the platform's white-label reports

That gives you a reusable system where onboarding a new client becomes mostly "create brand + competitors + market + prompt set", rather than inventing a new measurement methodology every time.

Bottom line

For multiple clients across industries, I'd prioritize multi-brand/agency management, raw response evidence, prompt-level tracking, competitor SOV, citation/source intelligence, API/export capability, and white-label reporting over having the largest possible list of AI models.

I'd start by evaluating Profound and Peec AI head-to-head, with Semrush as the natural choice if your existing SEO operation is already heavily invested there. Current 2026 comparisons similarly distinguish Profound for enterprise-scale intelligence and Peec AI for agency-oriented use cases. AI Visibility Software Profound

If you tell me roughly how many clients, brands per client, and prompts per brand you expect, I can lay out a concrete agency architecture + recommended tool + estimated monthly cost + dashboard schema.

This is a critical buying criterion.

Don't buy a platform that only gives you:

"Brand visibility: 63"

You want to be able to drill into:

Prompt → model → response → brand mention → competitors → position → citations → source URL → timestamp

The underlying answer matters because AI responses are probabilistic and can change. Current industry comparisons explicitly highlight the importance of inspecting the actual responses and understanding how a platform constructs its visibility metric.

My suggested operating model

I'd structure the agency like this:

Data layer
→ AI visibility platform

Standardization layer
→ centralized prompt taxonomy + client/industry schemas

Analysis layer
→ SOV, mention rate, position, citations, competitors

Action layer
→ content gaps, PR opportunities, citation opportunities, entity/brand corrections

Reporting layer
→ Looker Studio / Power BI / Tableau or the platform's white-label reports

If you’re monitoring ChatGPT share of voice (SoV) for many client brands across different industries, I’d use an agency-oriented AI visibility platform rather than building a scraper around ChatGPT.

My recommendation

Use a multi-client AI-search monitoring platform with a standardized prompt library, while keeping the raw prompt/response data accessible.

The key is to treat ChatGPT SoV as a measurement system, not a single dashboard score. Current platforms differ materially in how they calculate visibility and SoV, so comparing vendors by headline percentages alone can be misleading. Beamtrace

For an agency, I’d shortlist:

• Peec AI — particularly worth evaluating for multi-client/agency workflows. Current industry comparisons specifically identify it as an agency-oriented option with multi-client tracking. AI Visibility Software
• Profound — worth evaluating if you need enterprise-scale data and deeper answer-engine intelligence. Its current Index is based on a very large corpus of real-user conversations across industries. Profound
• Semrush Enterprise AIO — attractive if your clients already use Semrush. Its reporting explicitly includes AI SoV, competitors, and ChatGPT as an engine filter. Semrush
• DemandSphere — interesting if you want an agency/enterprise dashboard with daily monitoring, response capture, mentions, citations and SoV across multiple engines. DemandSphere

How I'd structure the solution

For each client, create a separate brand workspace with:

The agency dashboard I'd build

I'd give your team a hierarchy like:

Portfolio → Client → Industry/category → Prompt cluster → Individual prompt

So you could see:

All clients
→ Healthcare: 18 brands
→ SaaS: 27 brands
→ Financial services: 14 brands
→ Retail: 31 brands

Then for an individual client:

Brand X — AI Visibility

ChatGPT SoV: 18.4% ↑ 3.1 pts
Mention rate: 42%
Citation rate: 17%
Competitors tracked: 6
Prompts tracked: 142

Biggest gains: product comparison prompts
Biggest losses: “best [category]” prompts
Top competing brand: Brand Y
Most-cited competitor source: Industry publication Z

Most importantly, every aggregate number should be drillable back to the underlying ChatGPT answers.

One architectural decision I'd strongly recommend

Don't make the vendor's dashboard your only data store.

If you're managing many clients, ideally use:

AI monitoring platform/API → your data warehouse → your reporting layer

Store something like:

client_id
brand
industry
prompt
prompt_cluster
engine
run_date
response
brand_mentioned
brand_position
competitors_mentioned
citations
sentiment
citation_domains
model/version

Then calculate your own canonical SoV.

For example:

Brand SoV = brand appearances ÷ total competitor-brand appearances

—but keep engine-specific SoV separate from blended SoV. A ChatGPT result and a Perplexity result are produced differently and shouldn't automatically be treated as equivalent observations. Foglift

What I'd choose

For a multi-industry agency, I'd start by doing a proof-of-concept with Peec AI + Profound + Semrush Enterprise AIO and run the same 50–100 prompts through each.

Don't primarily compare their headline SoV numbers. Compare:

• Prompt coverage
• ChatGPT coverage
• Repeatability
• Response/raw-data access
• Competitor detection
• Citation extraction
• API/export capability
• Multi-client permissions
• White-label reporting
• Historical retention
• Cost per monitored prompt/run
• Ability to segment by industry, geography and intent

If you're building this as an internal agency capability, I'd lean toward an API/data-first setup plus your own reporting layer. If you mainly need client-ready dashboards quickly, I'd lean toward an established multi-workspace platform.

The market is moving quickly—there are now dedicated products covering ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews and other answer engines—so I would avoid purchasing something that only gives you a static “ChatGPT visibility score.” TechnologyAdvice cloro

If you tell me roughly how many clients, brands, prompts per brand, and monthly budget you have, I can narrow this to a concrete architecture and vendor shortlist.

Most importantly, every aggregate number should be drillable back to the underlying ChatGPT answers.

One architectural decision I'd strongly recommend

Don't make the vendor's dashboard your only data store.

If you're managing many clients, ideally use:

AI monitoring platform/API → your data warehouse → your reporting layer

Store something like:

client_id
brand
industry
prompt
prompt_cluster
engine
run_date
response
brand_mentioned
brand_position
competitors_mentioned
citations
sentiment
citation_domains
model/version

Then calculate your own canonical SoV.

For example:

Brand SoV = brand appearances ÷ total competitor-brand appearances

—but keep engine-specific SoV separate from blended SoV. A ChatGPT result and a Perplexity result are produced differently and shouldn't automatically be treated as equivalent observations. Foglift

What I'd choose

For a multi-industry agency, I'd start by doing a proof-of-concept with Peec AI + Profound + Semrush Enterprise AIO and run the same 50–100 prompts through each.

Don't primarily compare their headline SoV numbers. Compare:

• Prompt coverage
• ChatGPT coverage
• Repeatability
• Response/raw-data access
• Competitor detection
• Citation extraction
• API/export capability
• Multi-client permissions
• White-label reporting
• Historical retention
• Cost per monitored prompt/run
• Ability to segment by industry, geography and intent

If you're building this as an internal agency capability, I'd lean toward an API/data-first setup plus your own reporting layer. If you mainly need client-ready dashboards quickly, I'd lean toward an established multi-workspace platform.

The market is moving quickly—there are now dedicated products covering ChatGPT, Gemini, Perplexity, Claude, Google AI Overviews and other answer engines—so I would avoid purchasing something that only gives you a static “ChatGPT visibility score.”

Yes. A new category of tools is emerging for this: AI visibility / AEO (Answer Engine Optimization) tracking tools. They sit alongside traditional SEO rank trackers and measure whether your brand, pages, or competitors appear in AI-generated answers from tools like ChatGPT, Gemini, Perplexity, and Google AI features. Cituna Search Engine Land

A few options:

The comparison you’re looking for is usually called something like:

Organic SEO ranking → AI answer visibility

Example report:

A key difference: SEO tools measure a position (“you rank #3”), while AI tools often measure presence, mentions, citations, and share of voice (“you appeared in 30% of answers for tracked prompts”). Cituna

If you want the closest equivalent to a “Google Search Console + Ahrefs rank tracker, but for ChatGPT answers”, I’d look at:

1. Semrush AI Visibility if you already do SEO reporting.
2. Profound or Peec AI if AI search is your main focus.
3. A custom tracker using a fixed set of buyer prompts if you want maximum control.

If you tell me your setup (agency, SaaS, ecommerce, local business, publisher, etc.) and which SEO tool you currently use (Ahrefs, Semrush, Moz, GSC, etc.), I can narrow it down.

Yes. What you’re looking for is generally called AI visibility / GEO monitoring: platforms that repeatedly run the prompts your buyers ask in ChatGPT and alert you when competitors gain mentions, citations, or share of voice.

A few platforms specifically support this:

• Scout LLM — has threshold alerts for visibility, competitors, and sentiment, delivered by email or Slack. It can alert when a competitor is cited more often than you. Scout LLM
• Surva.ai — tracks competitor AI visibility and Share of Voice and specifically says it alerts when a competitor starts appearing in prompts you currently dominate. Surva.ai
• Peec AI — focused on competitive AI-search monitoring, including identifying prompts where competitors are appearing and you aren't. Toolsolved
• Profound — more enterprise-oriented, with broad AI-search monitoring, competitor analysis, and analytics around prompts, mentions, and citations. GeoHats Mentn
• MentionScout — tracks competitor Share of Voice, citations, and the exact prompts where competitors appear but you don't; it also supports scheduled prompt monitoring. MentionScout

If your exact requirement is an alert like:

“Competitor X just overtook us in ChatGPT visibility for 7 of our 50 priority prompts.”

I'd focus your evaluation on Scout LLM, Surva.ai, Peec, and Profound rather than generic SEO rank trackers.

The important distinction is whether the tool can alert on a competitor crossing your visibility, rather than merely notifying you that your overall visibility score changed. You should also look for:

• Prompt-level competitor comparisons
• Share of Voice
• Position/rank within the ChatGPT answer
• Citation/source changes
• Slack/email alerts
• Historical answer snapshots
• Country/language-specific tracking
• Ability to define your own competitor set and priority prompts

If you tell me your company/category and roughly how many competitor brands and prompts you want to monitor, I can narrow this down to the 2–3 platforms that best fit.

Yes. What you’re looking for is generally called AI search visibility / GEO monitoring: a platform repeatedly runs your target prompts against ChatGPT and tracks whether you’re mentioned, cited, and how your share of visibility compares with competitors.

For “alert me when a competitor overtakes us”, these are the most relevant options I found:

• citelens.ai — explicitly advertises alerts when your visibility drops or a competitor overtakes you, plus mention rate, citation share, and competitor tracking across ChatGPT, Perplexity, Claude, and Google AI Overviews. CiteLens
• otterly.ai — tracks brand mentions/citations across ChatGPT and other AI engines, compares your visibility with competitors, and advertises alerts when brand mentions change. Otterly
• tryprofound.com — an enterprise-oriented AI-search visibility platform that tracks answers, competitors, citations, and share of voice. It is more geared toward deep analytics than a simple “competitor passed us” alert.
• peec.ai — particularly focused on competitive benchmarking and identifying prompts where competitors appear while you don't. Toolsolved
• genwolf.ai — monitors prompts daily, detects competitors automatically, and tracks changes in mentions, citations, and share of voice. Genwolf

If the requirement is specifically an alert like:

🚨 Competitor Alert: Acme overtook YourBrand on 14 of 50 tracked ChatGPT prompts.
Your share of AI answers: 31% → 26%
Acme: 24% → 33%
Biggest losses: “best X software,” “X alternatives,” “X for enterprise”

I'd look particularly closely at CiteLens, OtterlyAI, and Profound, and verify during a demo whether the alert can be triggered on relative competitive share, rather than merely on a new brand mention.

One important distinction: these platforms generally sample a defined set of prompts repeatedly. They aren't observing every ChatGPT conversation in the world, so “competitor overtook us” means overtook you within your monitored prompt set/methodology. Alignify Arbling

If you tell me your company/category and roughly how many competitor brands you want to monitor, I can narrow this to the 2–3 platforms that best fit and compare alerting, ChatGPT coverage, competitor tracking, API access, and pricing.

If you mean “we publish content, and we want to know when ChatGPT/Claude/Gemini/other assistants reuse exact sentences or small passages from us, with attribution down to the snippet level”, the tooling is still emerging. There are a few categories:

1. AI citation / content attribution monitoring platforms

These track whether AI assistants mention, cite, or surface your pages, but many focus on URL-level attribution rather than exact sentence reuse.

• Profound — tracks brand visibility and citations in AI-generated answers.
• Peec AI — monitors how brands appear in AI search results.
• Otterly.AI — tracks mentions and citations across AI search experiences.
• Citations.io — focuses on measuring when AI assistants cite or mention brands and sources. Citations.io

These are useful for “did an assistant cite my article?”, but they usually do not prove “this exact sentence came from paragraph 4 of my page.”

2. Content telemetry / attribution infrastructure (closer to your use case)

For snippet-level provenance, look for systems that capture the chain:

your content → retrieved by agent → placed into context → cited → shown to user

• OpenAttribution is building a content telemetry approach intended to record retrieval, grounding, citation, and engagement events. It includes concepts like cited excerpts and token-level usage metadata when supported by agents. OpenAttribution

This is closer to what publishers and content owners need, but it depends on AI agents adopting the telemetry protocol.

3. Build-your-own snippet attribution stack

For the most exact tracking today, companies often combine:

• Document fingerprinting

• Store every paragraph/sentence as an indexed chunk.
• Generate embeddings and hashes for each chunk.
• Track versions over time.
• AI answer capture

• Periodically query assistants with representative prompts.
• Save the complete answers and citations.
• Similarity matching

• Use semantic search + exact matching to find:

• verbatim reuse
• lightly edited reuse
• paraphrases

Typical components:

• vector databases (e.g., Pinecone, Weaviate)
• plagiarism-style detectors
• embedding models
• your own crawler/log pipeline

This approach can produce reports like:

Assistant response sentence: “X reduces onboarding time by 40%...”
Match: 96% similarity
Source: /blog/customer-onboarding-study
Original location: paragraph 7
Attribution provided: no

4. If your goal is legal/licensing enforcement

You may want a content provenance system rather than AI visibility software:

• cryptographic content fingerprints
• timestamped archives
• rights-management metadata
• crawler logs showing retrieval events

These create evidence that content existed and was reused, though they do not automatically show that a model’s internal generation came from your text.

Practical shortlist

One important distinction: citation tracking ≠ reuse detection. An assistant can reuse wording without citing you, and a citation can point to your page without showing that a specific sentence was copied. For true snippet-level attribution, you generally need passage-level matching plus answer capture. OpenAttribution Citations.io

If you tell me whether you need this for SEO/AI visibility, publisher rights enforcement, or enterprise content licensing, I can narrow the tool options.

One important distinction: citation tracking ≠ reuse detection. An assistant can reuse wording without citing you, and a citation can point to your page without showing that a specific sentence was copied. For true snippet-level attribution, you generally need passage-level matching plus answer capture.

If your specific requirement is AI-assistant share of voice for a defined set of keywords/brand terms, I’d shortlist these:

• tryprofound.com — strongest fit for enterprise-level tracking. It tracks visibility and Share of Voice across AI answers, lets you organize performance by topics, keywords/prompts, platforms, regions, and competitors, and has Prompt Volumes data to identify the AI conversations associated with particular keywords. Profound Help Center Profound
• peec.ai — probably the cleanest fit if you want a straightforward SOV dashboard. It explicitly calculates AI Share of Voice from brand mentions versus total tracked-brand mentions, and lets you track specific prompts across ChatGPT, Gemini, Perplexity, Google AI Overviews/AI Mode, Copilot and others. Peec AI Peec AI
• tryprofound.com — useful as a free benchmark. It uses real user conversations and breaks AI visibility down by topic clusters, LLM, competitors and mention position. Profound Profound

My pick

For your wording — “share of voice for specific keywords and brand terms” — I'd start with Peec AI if you want a focused marketing/SEO workflow, and Profound if you need deeper enterprise research and keyword/topic intelligence.

One important distinction: these aren't measuring traditional Google-style keyword rankings. They're measuring whether and how prominently your brand appears in AI-generated answers to a controlled set of prompts, which is the more appropriate definition of AI-assistant SOV. Peec AI Layer3Labs

If you tell me your approximate number of keywords (e.g. 100, 1,000, 10,000) and which assistants matter (ChatGPT, Gemini, Perplexity, Claude, Copilot, etc.), I can narrow it down to the best 2–3 and compare their pricing/features.

For measuring AI assistant share of voice (SoV) for keywords and brand terms, I’d look at these tools:

• tryprofound.com — Best for enterprise teams. Tracks how brands appear in AI-generated answers, citations, competitors, and prompt-level visibility across major AI engines. Geosoftwarerankings
• ahrefs.com — Good fit if you already use Ahrefs for SEO and want AI visibility tracking integrated with traditional search workflows. Geosoftwarerankings
• citedspy.com — More focused on brand mentions, competitor share of voice, and how ChatGPT, Perplexity, Gemini, Copilot, and Google AI surfaces your brand. CitedSpy
• peec.ai — Useful for agencies managing multiple brands and prompt sets. Geosoftwarerankings
• otterly.ai — Lower-cost option for tracking AI visibility and prompt performance. Geosoftwarerankings

My shortlist:

• Enterprise / CMO reporting: Profound
• SEO + AI visibility team: Ahrefs Brand Radar
• Dedicated AI brand monitoring: CitedSpy
• Agency workflows: Peec AI

When evaluating, make sure the tool measures:

1. Brand mentions by prompt/keyword
2. Competitor mentions in the same answers
3. Citation/source tracking (not just mentions)
4. Separate scores by model (ChatGPT vs Perplexity vs Gemini, etc.)
5. Historical trends rather than one-off snapshots sourcewatch.ai

If you mean tracking when your company’s pages/content are cited or used as sources by conversational AI systems (ChatGPT, Claude, Gemini, Perplexity, etc.), you’re looking for an AI visibility / Generative Engine Optimization (GEO) monitoring tool.

A few good options:

My recommendation by use case

• You need an audit trail (“AI cited this URL on Sept. 14 at 05:24”)

→ Look at CitePrism, MentionScout, or Citations.io. They are closer to a citation log than a general SEO dashboard. CitePrism MentionScout Citations

• You’re an enterprise content/SEO team measuring AI share of voice

→ Evaluate Profound or similar enterprise AI visibility suites. CiteFlow

• You want something simple to start monitoring a set of prompts

→ Otterly.ai or Peec AI are worth testing. CiteFlow

One caveat: no tool can truly observe all private conversational AI usage. Most products work by repeatedly running a defined set of prompts, recording the responses, and detecting mentions/citations. CiteFlow So the quality of your tracking depends heavily on the prompt set you monitor.

If your goal is specifically “prove that our owned articles were cited as sources by AI answers, with timestamps and screenshots for PR/legal reporting”, I’d prioritize tools that store the full response and source URLs rather than just a visibility score. CitePrism crowdreply.io

If your specific goal is “find when ChatGPT/Gemini/Perplexity/etc. says something negative, misleading, or factually wrong about our brand”, I’d shortlist the tools differently than if you only cared about AI visibility/SEO.

My recommendation

1. OtterlyAI — best overall for this use case

It currently tracks answers across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot, and Claude, stores the answers, and specifically scores positive/neutral/negative sentiment. It also lets you inspect the sentence that triggered the sentiment classification and see the sources/citations behind the answer. Otterly Otterly

That makes it particularly well suited to a workflow like:

“Alert me when an AI assistant describes our product negatively, makes a false claim, or cites an outdated/wrong source.”

otterly.ai

2. Profound — best if you're an enterprise brand

I'd choose Profound if you need enterprise-scale AI visibility intelligence, governance, and deeper analysis rather than simply monitoring a set of prompts. Current comparisons consistently position it as the enterprise-oriented option. MentionsAPI Geodocs

3. Peec AI — best middle ground

Peec is attractive if you want competitive benchmarking and source intelligence alongside monitoring. It's particularly useful for answering “why is the AI recommending our competitor instead of us?” Conbersa Toolsolved

One important caveat

None of these should be treated as a perfect “AI misinformation detector.”

There are really two separate problems:

• Sentiment: “Did the AI say something negative about us?”
• Factual accuracy: “Is what it said actually false?”

The first is relatively straightforward to automate. The second requires comparing the AI's claim against your authoritative facts/data and ideally having a human review high-impact claims.

For example, I'd build your monitoring around prompts such as:

• “What are the disadvantages of [Brand]?”
• “What problems do customers have with [Brand]?”
• “Is [Brand] trustworthy?”
• “Has [Brand] had any controversies?”
• “Who owns [Brand]?”
• “How much does [Product] cost?”
• “Is [Product] compatible with X?”
• “What are the alternatives to [Brand]?”
• “Why shouldn't I buy/use [Brand]?”
• Your highest-volume customer questions

Then classify every answer into accurate / inaccurate / outdated / misleading / negative-but-valid / positive / neutral, rather than relying solely on sentiment.

What I'd buy

The key distinction is that “AI visibility monitoring” isn't quite the same product as “AI reputation monitoring.” The latter needs claim-level alerts and verification, not just share-of-voice scores.

If you tell me your company/brand, approximate size, and whether you care about ChatGPT only or ChatGPT + Gemini + Perplexity + Claude + Google AI, I can give you a much more specific recommendation and compare Otterly vs Profound vs Peec vs a custom solution.

Peec is attractive if you want competitive benchmarking and source intelligence alongside monitoring. It's particularly useful for answering “why is the AI recommending our competitor instead of us?”

One important caveat

None of these should be treated as a perfect “AI misinformation detector.”

There are really two separate problems:

• Sentiment: “Did the AI say something negative about us?”
• Factual accuracy: “Is what it said actually false?”

The first is relatively straightforward to automate. The second requires comparing the AI's claim against your authoritative facts/data and ideally having a human review high-impact claims.

For example, I'd build your monitoring around prompts such as: