Answers that name a competitor

If you mean tracking how AI assistants (ChatGPT, Claude, Gemini, Perplexity, etc.) describe your brand and whether that description becomes more positive or negative over time, the category is usually called AI brand sentiment monitoring, LLM visibility tracking, or GEO (Generative Engine Optimization) monitoring.

Tools that do this include:

• scrunch.com — Tracks sentiment in AI responses across platforms and can show positive/mixed/negative trends, including by model, prompt, topic, and competitor. Scrunch
• tryprofound.com — Provides AI brand sentiment dashboards, trend charts, prompt-level analysis, and insight into narratives shaping how AI describes your brand. Profound
• pi-datametrics.com — Tracks AI-generated mentions, sentiment distribution, and changes in AI brand perception over time. Pi Datametrics
• saidly.ai — Focused specifically on what AI assistants say about brands, including sentiment scores, model-by-model comparisons, quotes, and trends. Saidly
• gumshoe.ai — Measures brand visibility and sentiment across multiple AI models and buyer scenarios. Gumshoe AI
• livesov.com — Tracks mentions, recommendations, sentiment, and trends across major AI assistants. Livesov

For choosing one:

• Marketing/brand team: Profound or Scrunch are closer to a dashboard/reporting workflow. Scrunch Profound
• SEO/GEO team: Pi Datametrics, Gumshoe, or Livesov are more focused on AI visibility and competitive positioning. Pi Datametrics Gumshoe AI Livesov
• Smaller teams wanting direct “what does AI say about us?” tracking: Saidly or similar lightweight monitors may fit better. Saidly

A useful metric set to look for:

• Sentiment score over time (positive/neutral/negative)
• Share of voice vs competitors
• Mention frequency in AI answers
• Recommendation rate (“would AI suggest us?”)
• Which prompts produce negative framing
• Which sources/citations influence AI answers

If you mean traditional social listening sentiment (people posting about your brand, not AI assistants talking about it), that is a different tool category.

Yes — the category you want is AI search / GEO brand-visibility tracking.

Two particularly good fits:

• tryprofound.com — probably the closest match to your description. Its Sentiment feature tracks whether AI talks about your brand positively or negatively, shows daily sentiment trends, recurring themes, competitor comparisons, and the sources driving those narratives. It monitors ChatGPT, Claude, Gemini, Perplexity and other answer engines. Profound Profound
• peec.ai — tracks mention frequency, visibility, position, and sentiment across ChatGPT, Gemini, Perplexity, Google AI, Claude and others. It gives a 0–100 sentiment score and lets you see how perception changes over time. Peec AI Peec AI

My pick

If your primary question is “Is ChatGPT/AI becoming more or less positive about my brand over time, and why?”, I'd start with Profound. Its sentiment dashboard is specifically designed around positive/negative trends and the narratives and sources causing them. Profound

If you want broader AI-engine coverage plus a straightforward visibility/position/sentiment scorecard, Peec AI is a strong alternative. Peec AI

If you tell me your brand and industry, I can also compare the best 3–5 tools for your specific use case (including pricing and which AI models they actually track).

If your specific goal is monitoring brand sentiment in AI-generated answers across multiple assistants, I’d shortlist these:

1. OtterlyAI — my pick for most teams

Otterly is particularly well matched to your question because its analytics explicitly score brand mentions, sentiment, share of voice, position, competitors and citations. It runs the same prompt set across multiple AI engines, letting you see whether the tone of the answer changes from one assistant to another. otterly.ai

For example, you can monitor:

“What are the best [category] software platforms?”

and see:

• Is my brand mentioned?
• Positive / neutral / negative sentiment
• What competitors are mentioned?
• Where does my brand rank?
• Which sources are being cited?
• How does ChatGPT differ from Gemini, Claude, Perplexity, etc.?
• Is sentiment improving over time?

Otterly currently says it monitors ChatGPT, Perplexity, Google AI Overviews, Google AI Mode, Gemini, Copilot and Claude, with daily monitoring. otterly.ai

Best if: you're a brand/marketing team that wants a relatively straightforward dashboard rather than a giant enterprise platform.

2. Profound — best if this is becoming an enterprise intelligence program

Profound is more sophisticated if you want to go beyond "are we mentioned?" Its Answer Engine Insights tracks visibility, sentiment, share of voice, positioning, citations and competitors, and lets you segment results by things such as region, topic and audience persona. help.tryprofound.comwww.tryprofound.com

It also covers a particularly broad set of answer engines, including ChatGPT, Perplexity, Claude, Gemini, Copilot, Google AI Overviews/AI Mode, Grok and DeepSeek. Importantly, Profound says it queries the consumer-facing experiences rather than simply model APIs, which is useful if you care about what an actual user sees. help.tryprofound.comwww.tryprofound.com

Best if: you're managing a large brand, multiple markets, hundreds/thousands of prompts, or need serious competitive and executive reporting.

3. Peec AI

I'd put Peec on the shortlist if you're primarily an in-house marketing/SEO team or agency looking for configurable AI visibility tracking, sentiment, share of voice, competitor monitoring and reporting. Independent 2026 comparisons also put it among the stronger options for configurable multi-engine tracking. getrefine.ai

What I'd choose

Small/mid-size marketing team → OtterlyAI

Enterprise / global brand → Profound

Agency managing many brands → Peec AI or Profound

Already heavily invested in SEO/Semrush → Semrush AI Visibility

One important distinction: AI sentiment monitoring isn't the same thing as social listening. These platforms are measuring how AI assistants describe your brand when prompted, rather than how humans feel about your brand on Reddit, X, news sites, etc. That's actually a very useful new metric because an AI can consistently characterize a brand negatively even when conventional social sentiment looks fine.

And this data can move surprisingly quickly: recent analysis found substantial changes in which sources ChatGPT cites over just a few weeks, illustrating why longitudinal monitoring across multiple assistants is more useful than occasional manual checks. www.axios.com

If I were buying today, I'd demo Otterly and Profound side-by-side using the same 50–100 prompts and compare their raw answers, sentiment classifications, competitor detection, citation tracking, geographic controls, and historical data. That will tell you much more than comparing feature checklists.

If your goal is specifically monitoring how AI assistants talk about your brand—including whether the sentiment is positive/negative, how often you're mentioned, what competitors are recommended, and which sources are cited—you want an AI visibility / AEO monitoring platform, not traditional social-listening software.

My shortlist

My pick: Profound

For a serious brand-monitoring program, I'd start with Profound. Its Answer Engine Insights product explicitly measures sentiment, visibility, citations, share of voice and positioning, while querying the actual answer engines on a recurring basis. Profound Knowledge Base

That distinction matters: you don't merely want to know "Did ChatGPT mention us?" You want something closer to:

What does ChatGPT say about us? Is that portrayal favorable? How does it compare with competitors? Which prompts produce negative perceptions? Which sources are causing that perception? Is the answer changing over time?

Profound currently covers a particularly broad set of AI surfaces, including ChatGPT, Claude, Gemini, Perplexity, Copilot, Grok, DeepSeek and Google's AI experiences. Profound Profound

One important caveat

"Sentiment" in AI answers isn't quite the same as social-media sentiment. A brand can be mentioned positively but still lose the recommendation, for example:

"Brand X is reliable, but Brand Y offers better value."

So I'd prioritize platforms that let you analyze sentiment + recommendation/positioning + share of voice + citations, rather than buying a tool based solely on a positive/neutral/negative score.

If you tell me your company size and approximate monthly budget (e.g. <$100, $100–500, $500–2k, enterprise), I can narrow this to the top 2–3 options and compare pricing, assistant coverage, sentiment accuracy, alerts, API access, and reporting.

For your use case, I’d recommend OtterlyAI.

Why it fits:

• Captures actual AI answers from ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Claude, and Copilot. otterly.aihelp.otterly.ai
• Tracks local/business mentions, citations, competitors, and how prominently your business appears.
• Scores each mention positive, neutral, or negative, then rolls that into a Net Sentiment Score from −100 to +100. otterly.aihelp.otterly.aihelp.otterly.ai
• Lets you define prompts such as “best [service] near me,” “best [service] in Las Vegas,” or “which [category] businesses should I consider?” and monitor them daily. help.otterly.ai
• Built-in PDF/CSV reporting, API access, and Looker Studio integration make it suitable for recurring client/management reports. otterly.ai
• Pricing currently starts at $29/month, with a free trial advertised. otterly.ai

Runner-up: Profound. It has particularly strong sentiment/theme analysis and can show which sources are driving positive or negative narratives, but I'd start with OtterlyAI if the priority is a straightforward “what are AI engines saying about my local business, and how positive is it?” reporting workflow. www.tryprofound.com

One caveat: these tools measure AI-generated answers to the prompts you configure, not every spontaneous mention of your business in every AI conversation. For a local business, prompt selection and geographic targeting are therefore important. otterly.aihelp.otterly.ai

If you tell me what type of local business you run, I can suggest the exact 10–20 prompts I'd track to measure its AI visibility and sentiment.

The closest fit is otterly.ai.

It specifically monitors how AI assistants/search engines describe your brand across ChatGPT, Gemini, Perplexity, Google AI Overviews/AI Mode, Copilot, and Claude. It scores mentions as positive/neutral/negative and provides a net sentiment score plus prompt- and engine-level breakdowns. Otterly Otterly

For your use case, you could:

• Track a set of customer-relevant prompts, e.g. “best alternatives to [brand]” or “is [brand] reliable?”
• Monitor them automatically each day. Otterly
• Set a threshold such as “alert us if net sentiment drops 15% week-over-week”.
• Send the alert to your marketing team's Slack, Zapier, or Make workflow. OtterlyAI says its API can trigger alerts/workflows through those tools. Otterly

So if the requirement is specifically “tell marketing when AI-generated brand sentiment starts deteriorating,” OtterlyAI is a strong direct match, rather than a conventional social-listening platform.

otterly.ai

If your goal is specifically “alert the marketing team when AI assistants start talking about our brand more negatively,” I’d look at Profound first.

tryprofound.com tracks brand sentiment across AI answer engines including ChatGPT, Claude, Gemini, Perplexity, Copilot, Grok and others. Its Sentiment feature tracks positive/negative sentiment over time, identifies the themes driving negative perception, and shows the source citations behind those claims. Profound Knowledge Base Profound

The particularly relevant piece is its Sentiment Score data, which can be used in Profound Agents to monitor negative ratios and sentiment changes. Profound also supports workflows that can turn those signals into actions. Profound Knowledge Base

How I'd set it up

• Track a fixed set of high-value prompts about your brand and competitors.
• Monitor negative sentiment ratio and key negative themes daily.
• Set an alert threshold, e.g. negative sentiment increases >20% week-over-week or a new negative theme crosses a certain number of mentions.
• Route the alert to the marketing/PR team, ideally with the exact AI response and cited sources attached.
• Have the team investigate whether the cause is bad press, reviews, outdated content, product issues, or an inaccurate AI narrative.

Alternative: nightwatch.io is worth considering if you want a simpler monitoring-oriented product. It explicitly offers AI brand mention alerts plus positive/neutral/negative sentiment and context across ChatGPT, Claude, Gemini and Perplexity. Nightwatch

My pick: Profound if your marketing team wants not just an alert, but the “why did sentiment turn negative and what should we do about it?” analysis.

Yes. There are now several tools specifically built for this, usually under AI visibility / answer-engine optimization (AEO/GEO).

For your exact requirement—visualizing sentiment trends for your brand in AI-generated answers, broken down by region and language—these stand out:

• getmentions.ai — Tracks sentiment over time across AI platforms, with breakdowns by market and language, plus competitor benchmarking. This is probably the closest match to your description.
• semrush.com — Tracks sentiment, narratives, topics and share of voice, with 68,000+ location-language combinations. Strong choice if you're already using Semrush.
• menra.ai — Daily monitoring across major AI engines, with sentiment scores and regional drill-downs including US, EU, MENA, SEA, etc.
• kime.ai — Specifically offers sentiment trends across markets, languages and AI models, with competitor comparisons.
• qwairy.co — More focused on AI brand perception: sentiment trends, anomaly detection, competitor sentiment, and filters for engine, country, language and topic.
• brandcitation.com — Particularly interesting if you care about MENA: it tracks sentiment, mentions and share of voice across AI answers with separate English/Arabic visibility.

If you're looking for a dashboard that answers something like:

“Is AI becoming more positive or negative about our brand, and is that different in Germany vs. the US vs. Japan?”

I'd shortlist GetMentions, Semrush, and KIME first. If narrative/brand-perception analysis matters more than pure visibility, I'd also look closely at Qwairy.

One important distinction: these tools generally measure the sentiment expressed in sampled AI answers, not actual consumer sentiment or the sentiment of all AI conversations. The quality of the trend therefore depends heavily on the prompt set, sampling frequency, models, geography, and language coverage. GetMentions AI KIME

If you tell me roughly how many countries/languages and which AI platforms you need to monitor, I can narrow this down to the 2–3 best options.

Yes. There’s now a fairly mature category of AI visibility / GEO (Generative Engine Optimization) monitoring tools that specifically tracks how AI assistants describe brands—not just whether they mention them.

For your exact requirement—sentiment trends for your brand across AI answers, regions, and languages—I’d shortlist:

• getmentions.ai — Probably the closest match. It tracks sentiment, visibility and ranking across AI platforms, with breakdowns by market and language, plus competitor benchmarking and historical trends. Mentions
• menra.ai — Particularly strong for regional analysis. It runs prompts across ChatGPT, Perplexity, Claude, Gemini, Copilot, etc., and provides sentiment scores plus regional drill-downs. Menra
• qwairy.co — More brand-perception oriented. It supports sentiment trends, anomaly detection, competitor sentiment, and filtering by AI engine, country, language, and topic. It claims 45+ languages and 100+ countries. Qwairy
• buzzsense.ai — Explicitly offers sentiment by model and market, including languages, alongside share-of-voice and competitor monitoring. BuzzSense
• writesonic.com — Tracks sentiment, citations and share of voice and lets you filter by market, language, intent, competitor and date, with trend reporting. Writesonic
• birdeye.com — Interesting if you're a multi-location/local brand. Its sentiment reporting explicitly breaks down positive/neutral/negative AI mentions over time and across locations. Birdeye Help Center

What I'd look for

The important distinction is whether the product gives you merely a sentiment score or lets you drill from:

Region → language → AI model → prompt/topic → actual answer → sentiment rationale

The latter is much more useful. For example:

Germany / German / ChatGPT / “best CRM for mid-market companies”
→ Brand mentioned #2
→ Sentiment: negative
→ Reason: “expensive implementation”
→ Competitor #1: positive
→ Sources cited by ChatGPT: X, Y, Z

That lets you actually diagnose why AI perception differs between markets.

If you're evaluating this for an enterprise brand, GetMentions, Menra, and Qwairy are the three I'd investigate first. If you tell me roughly how many countries/languages and which AI platforms you need to monitor, I can narrow this down and compare their coverage, pricing, and dashboards.

Yes. What you’re describing is generally called LLM/AI brand monitoring or AI search visibility monitoring: software repeatedly runs buyer-style prompts against AI assistants, captures the answers, detects your company’s mentions, and labels the context/sentiment.

A few strong options:

• gumshoe.ai — particularly relevant if you want to see how AI talks about your company. It monitors ChatGPT, Gemini, Claude, Perplexity and others, with mention frequency, recommendation rate, sentiment, competitor comparisons, and persona-based conversations. Gumshoe AI
• nightwatch.io — tracks ChatGPT, Claude, Gemini and Perplexity, including exact surrounding context, positive/neutral/negative sentiment, competitor mentions and alerts. Nightwatch
• demandsphere.com — more enterprise-oriented; monitors 10+ AI engines and tracks mentions, citations, sentiment and competitive positioning. DemandSphere
• foglift.io — good if you want an API/developer workflow. It tracks prompt-level answers, mentions, citations, sentiment and competitors across ChatGPT, Perplexity, Claude, Gemini and Google AI surfaces, with API/CLI/MCP access. Foglift Foglift
• modelmention.io — focused on ChatGPT, Gemini and Perplexity, with visibility, share of voice, sentiment, citations and prompt-level tracking. ModelMention
• useklarity.co — runs defined buyer prompts across ChatGPT, Gemini and Perplexity and reports share of voice and sentiment per mention. Klarity

If your specific goal is “extract snippets”

I'd shortlist Nightwatch, Gumshoe, and Foglift.

There's an important distinction: most of these tools don't literally "crawl ChatGPT" like a web crawler. They submit a controlled set of prompts to AI engines and store the resulting answers, then analyze those answers. That is generally much more useful for reproducible monitoring. Foglift, for example, explicitly describes saving buyer prompts and running them across engines to measure mentions, citations, sentiment and share of voice. Foglift

For a typical monitoring pipeline, I'd want the output to look roughly like:

One caveat: if by “popular AI assistants” you mean actual user conversations rather than answers generated from your monitoring prompts, that's a different—and much harder—product. These platforms generally monitor controlled queries, not private conversations users are having with ChatGPT/Gemini.

If you tell me your company type, approximate budget, and whether you need an API/export, I can narrow this to the 2–3 best choices and compare their coverage, snippet extraction, sentiment, pricing, and API capabilities.

The category you’re looking for is usually called AI brand monitoring, LLM visibility tracking, or GEO/AEO monitoring (Generative Engine Optimization / Answer Engine Optimization). These tools generally don’t “crawl” the private internals of ChatGPT, Claude, or Gemini; instead, they run controlled prompts against AI assistants (often via APIs or automated sessions), capture the answers, extract company mentions, and score things like sentiment, rank/position, citations, and competitor mentions. MentionsAPI Foglift

Some options:

If you want to build your own pipeline, the typical architecture is:

1. Prompt library

• “best tools for [category]”
• “[company] alternatives”
• “is [company] trustworthy?”
• “[company] reviews”
• AI assistant runners

• APIs where available (OpenAI, Anthropic, Google, etc.)
• Browser automation only where permitted by the service terms
• Extraction layer

• Detect company mentions
• Pull surrounding snippets (e.g., ±1–3 sentences)
• Identify competitors mentioned nearby
• Sentiment classifier

• Positive / neutral / negative
• Topic tags (“pricing”, “support”, “quality”, “security”)
• Confidence score
• Dashboard

• Mention frequency
• Sentiment trend
• Share of voice
• Which prompts produce negative descriptions

For a marketing team, I’d start with a dedicated tool rather than scraping assistants directly, because AI responses vary by prompt, user context, model version, and whether the assistant has web access. Reddit

If you mean “crawl all publicly visible AI answers on the web” rather than “query AI assistants and analyze their outputs,” that is a different problem and usually involves web monitoring plus LLM extraction.

If your goal is specifically “Did the PR crisis/product launch change what AI assistants say about us?”, I’d use a dedicated AI-answer sentiment monitor, rather than conventional social listening alone.

My recommendation

Start with tryprofound.com if you want a mature PR/brand workflow. It tracks sentiment in AI-generated answers, surfaces the themes and sources driving the sentiment, and is designed to compare changes following PR/content events. Profound says its daily tracking can start showing the effect of a major campaign or press placement within roughly 48–72 hours. Profound

Two alternatives worth evaluating:

• brandi.ai — particularly interesting if you need source-level attribution: which articles/publishers are pulling AI sentiment up or down, and how individual sources change over time. PR Newswire
• llmpulse.ai — more directly positioned around crisis management, product launches, PR campaigns, and tracking how AI models describe your brand and competitors. LLM Pulse LLM Pulse

How I'd implement it

Don't reduce the measurement to one overall sentiment score. Create a pre/post event panel:

Then establish a 7–14 day baseline before the event, capture the same fixed set of prompts every day, and compare:

Baseline → event day → +1 day → +3 → +7 → +14 → +30

That matters because generative answers are nondeterministic; a single answer isn't strong evidence of a reputation change. Longitudinal evaluation and replay against a consistent prompt set are much more reliable ways to identify meaningful drift. flowscope LangChain

The dashboard I'd want

Something like:

Launch sentiment: +12% vs. baseline
Positive: 42% → 54%
Negative: 31% → 24%
Recommendation rate: 38% → 47%
Biggest improvement: product quality
Biggest remaining risk: pricing
Primary negative source: Publisher X
Most affected models: Perplexity, ChatGPT

I'd also connect it to your existing LLM observability/evaluation stack if the “AI answers” are your own product's answers rather than public AI answers. Tools such as mlflow.org support continuous evaluation, production traces, LLM-judge scoring, and drift detection. MLflow AI Platform

Bottom line: for a PR/brand team, I'd trial Profound first, with Brandi AI as the strongest alternative if source-level narrative attribution is especially important. If you're monitoring your own AI product's responses rather than how public AI engines perceive your brand, I'd take a different approach and use an LLM observability/evaluation platform instead.

Yes. This has become a new category of products, often called AI visibility, LLM observability, or Generative Engine Optimization (GEO). A few platforms go beyond simply counting citations and actually analyze sentiment, framing, and which source documents appear to drive positive or negative responses.

The closest matches to what you're describing are:

The really interesting capability is what you described:

"Which sources lead to positive answers versus negative answers?"

That's still fairly uncommon, but LLM Pulse and Wellows are closest. They don't just tell you that ChatGPT cited Reddit or TechCrunch—they let you see:

• the exact AI response,
• every cited URL,
• whether the response was positive, neutral, or negative,
• and which domains repeatedly appear in negative responses. Wellows LLM Pulse

That lets you answer questions like:

• "Whenever ChatGPT says our onboarding is confusing, which articles is it citing?"
• "Which review sites consistently produce negative framing?"
• "Which third-party sources are correlated with positive recommendations?"

What's still missing

I haven't seen a platform that fully models causality ("this source caused the model to be positive"). Current tools generally infer relationships by observing:

1. Prompt
2. AI answer
3. Citations used
4. Sentiment of the answer

Over many prompts, they can identify patterns such as:

But because LLMs synthesize information from multiple sources (and sometimes from internal knowledge), it's difficult to prove any single source caused a positive or negative answer. The platforms are measuring correlation rather than definitive attribution. Wellows Profound Knowledge Base

An opportunity I don't see anyone fully addressing

A product that would stand out would build a citation influence graph, for example:

Prompt
    ↓
Answer sentiment: +0.73
    ↓
Evidence graph

Official docs  +0.45
Customer reviews +0.21
GitHub +0.11
Reddit -0.08
News article -0.05

Then aggregate this across thousands of prompts to produce metrics like:

• "Positive citation score by domain"
• "Source trust lift"
• "Negative narrative contributors"
• "Topics each source influences"

That would be more actionable than today's dashboards because it answers not just who gets cited, but who shapes the model's opinion. I haven't found a mainstream product that exposes that level of influence analysis yet, although several are moving in that direction.

Yes. This has become a new category of products, often called AI visibility, LLM observability, or Generative Engine Optimization (GEO). A few platforms go beyond simply counting citations and actually analyze sentiment, framing, and which source documents appear to drive positive or negative responses.

The closest matches to what you're describing are:

The really interesting capability is what you described:

"Which sources lead to positive answers versus negative answers?"

That's still fairly uncommon, but LLM Pulse and Wellows are closest. They don't just tell you that ChatGPT cited Reddit or TechCrunch—they let you see:

• the exact AI response,
• every cited URL,
• whether the response was positive, neutral, or negative,
• and which domains repeatedly appear in negative responses. Wellows LLM Pulse

That lets you answer questions like:

• "Whenever ChatGPT says our onboarding is confusing, which articles is it citing?"
• "Which review sites consistently produce negative framing?"
• "Which third-party sources are correlated with positive recommendations?"

What's still missing

I haven't seen a platform that fully models causality ("this source caused the model to be positive"). Current tools generally infer relationships by observing:

1. Prompt
2. AI answer
3. Citations used
4. Sentiment of the answer

Over many prompts, they can identify patterns such as:

But because LLMs synthesize information from multiple sources (and sometimes from internal knowledge), it's difficult to prove any single source caused a positive or negative answer. The platforms are measuring correlation rather than definitive attribution. Wellows Profound Knowledge Base

An opportunity I don't see anyone fully addressing

A product that would stand out would build a citation influence graph, for example:

Prompt
    ↓
Answer sentiment: +0.73
    ↓
Evidence graph

Official docs  +0.45
Customer reviews +0.21
GitHub +0.11
Reddit -0.08
News article -0.05

Then aggregate this across thousands of prompts to produce metrics like:

• "Positive citation score by domain"
• "Source trust lift"
• "Negative narrative contributors"
• "Topics each source influences"

That would be more actionable than today's dashboards because it answers not just who gets cited, but who shapes the model's opinion. I haven't found a mainstream product that exposes that level of influence analysis yet, although several are moving in that direction.

If your goal is specifically “How does what AI assistants say about us affect website traffic and ultimately conversions?”, I’d shortlist these:

My pick: Semrush

Semrush is probably the best fit if you want to connect three layers:

AI answer → brand sentiment/visibility → website traffic → conversion

Its AI Visibility Toolkit tracks how ChatGPT, Gemini, Perplexity and other AI systems describe your brand, including share of voice, sentiment, prompts, citations and competitors. Semrush Semrush

More importantly, its enterprise offering explicitly supports connecting AI-search data with GA4 or Adobe traffic and conversion data. Semrush for Enterprise

semrush.com

Profound is the most interesting alternative

If AI answers themselves are the centerpiece of your analysis, I'd seriously consider Profound.

It combines answer-engine visibility and sentiment analysis with AI traffic attribution, including measuring human visitors and conversions coming from AI-driven search. Profound Profound

Its sentiment product also looks at the themes and attributes associated with your brand and the sources influencing those AI narratives. Profound Knowledge Base

tryprofound.com

There's an especially interesting reason to consider it: Profound recently published research attempting to measure AI exposure → subsequent browsing behavior, rather than relying only on clickable referral traffic. They found that standard referral attribution can substantially undercount AI's influence. Profound

Where Similarweb fits

Similarweb is better if you want a traffic/competitive intelligence-first view. It can identify AI traffic from ChatGPT, Perplexity, Gemini, Claude and others, show which pages receive it, and identify prompts associated with AI traffic. Similarweb Similarweb Knowledge Center

similarweb.com

What I'd build

For a serious measurement program, I'd structure the data roughly like this:

AI prompt
→ AI answer
→ brand mentioned?
→ sentiment
→ position/share of voice
→ citation/source
→ AI referral or subsequent branded search
→ landing page
→ conversion
→ revenue

Then compare cohorts such as:

• Positive AI sentiment vs. neutral/negative
• Brand recommended vs. merely mentioned
• High AI visibility vs. low visibility
• AI-cited pages vs. non-cited pages
• AI-exposed users vs. non-exposed users
• Changes in AI sentiment before vs. after conversion-rate changes

If you're trying to prove ROI to an executive team, I'd choose Semrush + GA4. If you're trying to understand and optimize the AI-answer layer itself, I'd choose Profound.

One caveat: don't equate AI referral traffic with AI influence. A large portion of AI-influenced visits may arrive later through direct, organic, or branded-search channels, so a good measurement setup should attempt to capture both. Profound's recent research illustrates why this distinction matters. Profound

Its AI Visibility Toolkit tracks how ChatGPT, Gemini, Perplexity and other AI systems describe your brand, including share of voice, sentiment, prompts, citations and competitors. Semrush Semrush

More importantly, its enterprise offering explicitly supports connecting AI-search data with GA4 or Adobe traffic and conversion data. Semrush for Enterprise

semrush.com

Profound is the most interesting alternative

If AI answers themselves are the centerpiece of your analysis, I'd seriously consider Profound.

It combines answer-engine visibility and sentiment analysis with AI traffic attribution, including measuring human visitors and conversions coming from AI-driven search. Profound Profound

Its sentiment product also looks at the themes and attributes associated with your brand and the sources influencing those AI narratives. Profound Knowledge Base

tryprofound.com

There's an especially interesting reason to consider it: Profound recently published research attempting to measure AI exposure → subsequent browsing behavior, rather than relying only on clickable referral traffic. They found that standard referral attribution can substantially undercount AI's influence. Profound

Where Similarweb fits

Similarweb is better if you want a traffic/competitive intelligence-first view. It can identify AI traffic from ChatGPT, Perplexity, Gemini, Claude and others, show which pages receive it, and identify prompts associated with AI traffic. similarweb.com support.similarweb.com

If by “AI answer sentiment data” you mean tracking how ChatGPT/Perplexity/Gemini/etc. describe or recommend your brand—including mentions, sentiment, citations, and share of voice—and then tying that to website traffic and conversions, my top pick is Profound.

My shortlist

Why I'd pick Profound

tryprofound.com specifically combines the pieces you're describing:

• AI answer data: tracks brand visibility, sentiment, citations, share of voice, positioning and competitors across answer engines. Profound Profound Knowledge Base
• Web traffic: its Agent Analytics identifies human traffic originating from AI systems. Profound
• Conversions/revenue: its GA4 integration can import conversion and revenue metrics so AI visibility can be correlated with business outcomes. Profound
• Page-level analysis: it can connect AI performance with individual web pages, which is particularly useful for figuring out which content changes actually matter. Profound Knowledge Base

The important distinction is that AI referral traffic alone isn't enough. A lot of AI influence won't appear as a clean chatgpt.com → yoursite.com referral. Profound's recent research, for example, found that only about 2.5% of downstream visits in its study carried a trackable AI-referral parameter, despite substantially higher observed browsing after AI mentions. Profound

So I'd structure the measurement around:

AI visibility → sentiment → citation/source changes → branded/direct/organic traffic → conversions → revenue

rather than simply:

AI referral → conversion

If you want the most rigorous setup

I'd actually consider:

Profound + GA4 + BigQuery + your CRM

GA4's BigQuery export provides user-, session-, and event-level traffic attribution data, which makes it possible to join AI visibility/mention data with conversion and revenue datasets. Google for Developers

That lets you answer questions such as:

“When our AI sentiment improves from neutral to positive for high-intent prompts, does branded traffic increase over the next 7 days, and does that ultimately increase demo requests?”

That's a much more valuable analysis than an “AI visibility score.”

If you're evaluating this for a company, I'd rank them: Profound #1 for exactly this use case, Semrush #2 if you're primarily an SEO organization, and custom GA4/BigQuery + an AI visibility platform if you have a data/analytics team and want serious causal/attribution analysis.

If you tell me your company size, B2B vs. B2C, approximate monthly traffic, and whether you use GA4/HubSpot/Salesforce, I can narrow this to the best 2–3 options and explain the data architecture I'd use.

Yes. What you’re describing is usually a combination of AI answer monitoring / LLM observability, sentiment & tone analysis, and intent classification. There are a few categories depending on whether you mean your own AI assistant responses or how ChatGPT/other AI assistants talk about your services.

1. Tools for scoring AI assistant responses (tone, quality, brand alignment)

These can evaluate generated answers against criteria like:

• positive/negative tone
• confidence level
• empathy
• brand voice compliance
• factuality / hallucination risk
• helpfulness

Examples:

• LangSmith — evaluates LLM traces, runs automated evaluators, and supports custom scoring rubrics.
• Arize AI — focuses on LLM evaluation, monitoring, and production quality signals.
• Humanloop — helps teams evaluate and improve AI outputs.

2. Tools for segmenting conversations by intent

For labels like:

• “review”
• “recommendation”
• “comparison”
• “pricing question”
• “competitor evaluation”
• “support request”

You can use:

• Rasa — intent classification and conversation analytics. Intent classification is a standard capability in NLU systems. arxiv.org
• Google Dialogflow — intent/entity classification.
• Microsoft Azure AI Language — custom text classification and sentiment analysis.

3. Tools specifically for “how is AI describing our brand/services?”

If you mean:

“When users ask ChatGPT, Claude, Gemini, etc. about our company, what tone does the answer have and what intent category was the query?”

That’s a newer category often called AI search visibility / answer engine optimization (AEO) monitoring. Look for tools that track:

• prompts asked about your brand
• AI-generated mentions
• competitor comparisons
• recommendation frequency
• sentiment/tone of AI answers

Examples include:

• Profound
• Scrunch AI
• Brandwatch (broader brand monitoring)

4. A common setup for your use case

A mature stack often looks like:

1. Collect AI responses

↓

2. Classify user intent:
   • Review
   • Recommendation
   • Comparison
   • Research
   • Purchase intent

↓

3. Score response:
   • Sentiment: -1 to +1
   • Tone: professional / enthusiastic / skeptical
   • Brand accuracy
   • Competitive positioning

↓

4. Dashboard trends:
   • “AI recommends us 42% of the time”
   • “Comparison prompts mention competitor X more often”
   • “Negative tone appears mostly in pricing queries”

If you mean monitoring ChatGPT/Gemini/Claude answers about your company, the closest fit is the AI search visibility category. If you mean evaluating your own chatbot’s replies, LLM evaluation platforms are the better fit.

If you mean AI assistant mentions specifically, Semrush’s AI PR Toolkit / Media Monitoring looks like the closest match: it lets PR teams filter mentions, inspect sentiment and AI citations, select mentions, and export them as CSV or Excel. Semrush

If by “clips” you mean actual broadcast/video clips, MediaIntel is a better fit—it searches video sources, identifies the exact spoken mention with timestamps, scores sentiment, and routes the clip/mention for monitoring. mediaintel.me

Short answer:

• Text/web/AI-assistant mentions + sentiment + export: Semrush
• Video/broadcast mentions + sentiment + timestamped clips: MediaIntel

If by “sentiment reports” you mean how major AI assistants describe/recommend your enterprise brand each day—rather than traditional social-media sentiment—the best enterprise solution is an AI visibility/brand-monitoring platform with scheduled prompt monitoring, not a conventional social-listening tool.

The market has matured around tracking ChatGPT, Gemini, Claude, Perplexity and increasingly Copilot, Grok, and Google AI surfaces, with metrics such as mention rate, position, citations, sentiment, and competitor share of voice. Birdeye Help Center Zumi

My recommendation

For a large enterprise, I'd shortlist:

If I were designing this for an enterprise

I'd make the daily report much more than “sentiment = 72%.” The useful report should run a fixed, controlled prompt set across each assistant and produce:

1. Brand visibility — % of answers mentioning the company.
2. Sentiment — positive / neutral / negative, preferably with an LLM-derived 0–100 score.
3. Recommendation rate — how frequently the assistant actually recommends you.
4. Position — #1 recommendation vs. #5 mention.
5. Competitor share of voice — who is winning the same prompts.
6. Key negative narratives — e.g. “expensive,” “poor support,” “limited integrations.”
7. Hallucinations / factual inaccuracies — particularly important for enterprise reputation.
8. Citations — which websites/content are causing the AI answer.
9. Day-over-day changes — especially newly appearing negative claims.
10. Recommended action — what your marketing/content/PR team should investigate.

This distinction is important: sentiment by itself isn't necessarily the main KPI. A brand can have highly positive sentiment but almost never appear. Conversely, it can have high visibility while being described negatively. Tracemetry

The architecture I'd use

For a Fortune-500-style organization, I'd aim for:

Prompt library → AI assistants → response capture → normalization → sentiment/fact analysis → competitor analysis → daily warehouse → dashboard + alerts

For example:

                    ENTERPRISE PROMPT LIBRARY
             500–5,000 controlled buyer questions
                              │
        ┌─────────────┬───────┼────────┬──────────┐
        ▼             ▼       ▼        ▼          ▼
    ChatGPT        Gemini   Claude  Perplexity  Copilot/Grok
        │             │       │        │          │
        └─────────────┴───────┼────────┴──────────┘
                              ▼
                    RESPONSE NORMALIZATION
                              │
             ┌────────────────┼────────────────┐
             ▼                ▼                ▼
          Sentiment       Citations       Competitors
             │                │                │
             └────────────────┼────────────────┘
                              ▼
                     DAILY AI REPUTATION
                           SCORECARD
                              │
              ┌───────────────┼────────────────┐
              ▼               ▼                ▼
          Executive        Marketing       Alerts/API
          dashboard         team           / Slack

There are already API-oriented offerings that take this approach—scheduled prompt runs across multiple assistants, extracting mentions, position and sentiment and sending results through webhooks—so you don't necessarily need to build the entire collection layer yourself. MentionsAPI

One important enterprise requirement

Don't rely exclusively on a single aggregate sentiment score.

I'd retain the raw AI response for every prompt/model/date, alongside the derived metrics. That gives you an auditable trail:

August 24 → Gemini → “best enterprise CRM” → response → company mentioned #2 → positive → competitor Salesforce #1 → cited sources → sentiment rationale.

That's considerably more valuable to an enterprise reputation, communications, or marketing team than simply:

Sentiment: 74

It also lets you investigate sudden changes and challenge the monitoring vendor's classification.

My choice

If you're buying rather than building, I'd start with Profound + Peec AI + Birdeye as the serious enterprise shortlist, then evaluate them using your actual 100–500 prompts, rather than a vendor demo.

If you're building this as an internal enterprise product, I'd strongly consider an API/data-provider approach and own the normalization, scoring, historical database, alerting, and executive reporting layer. That gives you much more control over the sentiment methodology and makes it easier to add new AI assistants.

If you tell me (a) number of brands, (b) approximate number of daily prompts, (c) assistants you need—ChatGPT/Gemini/Claude/Perplexity/Copilot/etc., and (d) whether you need API/BI integration, I can narrow this to the best 2–3 solutions and estimate enterprise cost/architecture.

If you mean “we changed page X on our website on Tuesday—did AI answers become more positive afterward, and can we trace that change back to the page?”, the closest fit I found is Profound.

Best fit: Profound

Profound is particularly strong for this because it combines:

• AI-answer sentiment tracking — positive/negative/neutral sentiment over time.
• Theme and claim analysis — identifies what AI is saying about you, not just a sentiment score.
• Citation-level drill-down — lets you see the sources/pages behind the claims influencing the AI narrative. help.tryprofound.com
• Daily prompt tracking across AI answer engines, with the actual answers and citation sources captured over time. www.tryprofound.com
• Content-to-sentiment workflows — Profound specifically describes tracking sentiment changes after content/product updates and feeding insights into content optimization. help.tryprofound.com

So you could structure an experiment like:

Jan 1: AI sentiment = +12
→ AI repeatedly says “expensive” and “limited integrations”
→ Those answers cite your pricing/integrations pages.

Jan 15: Update those pages.

Jan 22–Feb 15: Track the same prompts daily
→ “expensive” theme declines
→ “flexible pricing” appears
→ sentiment rises to +31
→ updated pages increasingly appear as citations.

That's much closer to content-change → AI narrative change attribution than a conventional brand-monitoring tool.

Runner-up: OtterlyAI

OtterlyAI is probably the simpler option if your primary need is monitoring sentiment + citations + visibility over time. It tracks prompts daily across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini, Copilot and Claude, and provides sentiment at both brand and prompt level. otterly.ai

Its particularly useful feature is website citation tracking, including which URLs AI actually cites and how citation positions change. otterly.ai

My take:

• Need causal/content attribution and root-cause analysis → Profound
• Need straightforward AI visibility + sentiment + citation monitoring → OtterlyAI

One caveat: neither can truly prove causality from a website edit alone. AI answers can change because of model updates, competitor content, third-party coverage, indexing changes, etc. The strongest setup is to maintain a timestamped content-change log and compare it against prompt-level sentiment, themes, and cited URLs—with unchanged prompts as your baseline.

If you mean “we changed specific pages on our website, and we want to know whether those changes caused AI answers about us to become more positive/negative”, the strongest fit I found is tryprofound.com.

It gets fairly close to that exact workflow:

• Tracks AI answers daily across ChatGPT, Perplexity, Gemini, Google AI Overviews, Claude, etc., for defined prompts. Profound Knowledge Base Profound
• Measures sentiment over time, including positive/negative themes and the specific claims AI is making about your brand. Profound Profound Knowledge Base
• Connects sentiment to citations, so you can see which pages/sources are influencing the narrative. Its newer sentiment system drills down to the citations behind individual claims. Profound
• Tracks watched URLs/pages, which is useful for isolating the pages you changed. Profound Knowledge Base
• Its Agent tooling can compare sentiment across time periods, including identifying changes after product/content updates. Profound Knowledge Base

One important distinction

If you specifically need causal attribution—e.g.:

“We rewrote /pricing on August 3 → AI sentiment for pricing prompts improved 18% over the next 14 days → these 7 answers began citing the revised page.”

I'd treat Profound as the measurement layer, but you'd want to maintain a content-change log alongside it (Git/CMS history, deployment timestamps, etc.). The available documentation shows strong correlation/drill-down capabilities, but I wouldn't assume it can prove causality from a website edit alone.

There is also jujugeo.com, which explicitly advertises a “What moved” feed and says it can identify when a citation follows a fix you applied. That makes it particularly interesting if your priority is connecting changes to subsequent AI-search movements. JujuGEO

My shortlist: Profound for deeper sentiment/claim/citation analysis; jujuGEO if the primary requirement is a straightforward “what changed after our fix?” workflow.

If you mean sentiment in AI-generated answers about your brand—rather than sentiment in social posts—my top recommendation is tryprofound.com.

Why it fits:

• Tracks positive/negative sentiment over time across AI-search prompts and platforms. Profound
• Lets you drill into the specific AI evaluations/answers behind a sentiment change.
• Identifies the themes and sources driving the change, including the publishers, reviews, and competitor claims influencing AI's narrative. Profound
• Its workflow is essentially “sentiment drops → identify narrative/source → decide what content or PR needs attention.”

Two alternatives worth considering

• llmpulse.ai — particularly good if your priority is “show me exactly which AI answers caused the decline.” It scores sentiment at the individual-response level and lets you inspect the full answer and its citations. LLM Pulse
• seerly.app — strong for prompt-by-prompt sentiment tracking, with the full response, engine, date, sentiment aspect, and cited URLs behind each score. Seerly

My pick: Profound if you want a mature brand/AEO monitoring platform with root-cause analysis; LLM Pulse if the most important requirement is tracing a sentiment drop directly to the offending AI answers.

If by “answers” you mean your own AI assistant's customer-support answers (e.g. detecting when users suddenly become less positive after a particular generated response), that's a different category—and I can recommend platforms specifically for that use case.

Yes. If your agency specifically wants to benchmark how AI assistants describe clients’ brands against a category average, I’d shortlist these:

• prominenceai.io — closest fit. Its sentiment product explicitly compares a brand’s AI sentiment score against a dynamic category average, with “Overperforming / Neutral / Underperforming” classification. It monitors ChatGPT, Google AI Overviews/AI Mode, Gemini, Perplexity and Copilot. Prominence AI
• semrush.com — strongest mainstream agency option. Its Brand Performance reports compare favorable AI sentiment across your brand and competitors and break perception down by AI platform and feature/category. Semrush Semrush
• gensiv.com — particularly interesting for agencies. It benchmarks visibility, rank and sentiment against competitors across six AI engines, and explicitly offers an agency package with 10 brands, competitor tracking, geo-targeting and white-label client reports. Gensiv
• pineprompt.com — good if you want a clean client-facing scorecard. It tracks Visibility, Position, Share of Voice and Sentiment across AI platforms, with agency-specific positioning and CSV export. Pineprompt
• projectquadrant.com — good for deeper AI-perception analysis. It reports sentiment, visibility and competitive gaps across ChatGPT, Perplexity, Gemini, Claude and others. Quadrant

My pick for an agency

If your requirement is literally:

“Client sentiment in AI assistants vs. the average sentiment for their category”

I'd start with Prominence AI. Its methodology is unusually aligned with that question: rather than treating “75/100 sentiment” as inherently good, it asks whether the brand is warmer or colder than its category baseline. Prominence AI

If you need agency-scale reporting and white-labeling, I'd investigate Gensiv alongside it. Gensiv

One important distinction: most platforms benchmark against a chosen competitive set, whereas a true category average requires a sufficiently broad, consistently sampled category dataset. For example, Presenc AI has published research based on 50,000+ AI responses across 18 industries and 2,400+ brands, which is closer to the kind of normative dataset you'd want for a genuine category benchmark. Presenc AI

If you're evaluating this for an agency product, I can also compare 5–7 platforms on category benchmarking, sentiment methodology, number of AI assistants, white-label reports, API access, pricing, and multi-client support.

Yes. If the agency specifically wants client brand sentiment in AI assistants benchmarked against a category average, I’d shortlist these:

My pick: Semrush

Semrush AI Visibility Brand Performance Reports is probably the closest match if you're looking for something an agency can put into a recurring client reporting workflow.

Its Perception reporting specifically measures favorable sentiment in AI platforms and compares your brand against competitors. It can break perception down by platform—ChatGPT, Perplexity, Gemini and Google AI Mode—and identify the recurring things AI praises or criticizes about the brand. Importantly, its sentiment analysis uses non-branded queries, which makes the comparison more meaningful for category-level perception. www.semrush.com

If the category average is the crucial requirement

I'd also look closely at Brainpan.AI. It publishes category-level AI visibility research across ChatGPT, Gemini, Perplexity, Copilot and Claude, rather than merely giving you a score for an individual client. That makes it interesting if your agency wants to tell a client:

"Your favorable AI sentiment is 72%, versus 61% for the category."

rather than simply:

"Your AI sentiment improved 8 points."

www.brainpan.ai

Search Agency is another particularly relevant option. Its measurement framework explicitly captures sentiment, mentions, citations and competitor performance across the major assistants, with repeatable prompt sets and a published methodology. search.agency

One important distinction

I'd separate "sentiment benchmark" from "AI visibility benchmark." A lot of platforms advertise an AI Visibility Score that combines mention rate, position, citations and sentiment. That's useful, but it doesn't necessarily give you a defensible category-average sentiment score.

For an agency, I'd want the platform to support:

1. Non-branded category prompts — e.g. "best X for Y"
2. Multiple assistants — at minimum ChatGPT, Gemini, Perplexity and ideally Claude
3. Positive / neutral / negative sentiment per mention
4. Category average or peer benchmark
5. Competitor comparison
6. Consistent prompt sets over time
7. Client-level dashboards/reporting
8. Export/API access if you're feeding results into your own agency reporting

For example, Rankr tracks visibility, position and sentiment across multiple models and reports sentiment on a 0–100 scale, while GeoVector combines competitive/category prompts with sentiment and citation intelligence. rankr.so

If you tell me your agency's client count and whether you need white-label dashboards/API access, I can narrow this to the 2–3 platforms I'd actually demo.

Yes — what you’re looking for is usually called AI brand monitoring / AI visibility tracking / GEO monitoring: tools that repeatedly query different AI assistants and tell you whether your brand appears, how it’s described, and the sentiment of that description by model.

My top pick: gumshoe.ai

Gumshoe looks particularly well aligned with your requirement because it explicitly breaks performance down by AI model and tracks mention frequency, recommendation rate, and sentiment across ChatGPT, Claude, Gemini, Perplexity, and others. It also runs conversations using defined buyer personas, rather than simply scraping anonymous AI sessions. Gumshoe AI

You could get something like:

That model-by-model sentiment view is the key thing I'd look for.

Other good options

• otterly.ai — probably the best-known straightforward monitoring option. It tracks brand mentions, visibility, competitors, citations, and sentiment across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini and Copilot. It also offers an API. Otterly
• nightwatch.io — particularly interesting if you already use Nightwatch for SEO. It explicitly reports mentions and sentiment across ChatGPT, Claude, Gemini and Perplexity. Nightwatch
• rankr.so — a simpler option focused on visibility, position and sentiment, with daily per-model breakdowns. Rankr
• lettertrace.com — interesting if you want something developer-oriented/open-source. It monitors Claude, ChatGPT and Gemini and reports sentiment and share of voice over time. Lettertrace
• mentionsapi.com — worth considering if you don't want a dashboard and instead want to pipe model-level mentions + sentiment into your own data warehouse/dashboard. Its API is specifically designed to query ChatGPT, Claude, Gemini and Perplexity and return mentions, position and sentiment. MentionsAPI

One important caveat

I'd not treat the sentiment score as ground truth. These products are generally running a set of prompts against the models and then classifying the resulting language. Because LLM responses are nondeterministic, a good system should run multiple samples per prompt/model, retain the raw response, and let you inspect why it classified something as positive/neutral/negative. Some newer monitoring systems explicitly use repeated sampling for this reason. apify.com

If your actual goal is “show me exactly what ChatGPT vs Claude vs Gemini say about our brand, with sentiment, over time”, I'd shortlist Gumshoe, Otterly, and MentionsAPI — with MentionsAPI being the most interesting if you want to build your own internal reporting layer.

Other good options

• otterly.ai — probably the best-known straightforward monitoring option. It tracks brand mentions, visibility, competitors, citations, and sentiment across ChatGPT, Perplexity, Google AI Overviews/AI Mode, Gemini and Copilot. It also offers an API. Otterly
• nightwatch.io — particularly interesting if you already use Nightwatch for SEO. It explicitly reports mentions and sentiment across ChatGPT, Claude, Gemini and Perplexity. Nightwatch
• rankr.so — a simpler option focused on visibility, position and sentiment, with daily per-model breakdowns. Rankr
• lettertrace.com — interesting if you want something developer-oriented/open-source. It monitors Claude, ChatGPT and Gemini and reports sentiment and share of voice over time. lettertrace.com
• mentionsapi.com — worth considering if you don't want a dashboard and instead want to pipe model-level mentions + sentiment into your own data warehouse/dashboard. Its API is specifically designed to query ChatGPT, Claude, Gemini and Perplexity and return mentions, position and sentiment.

One important caveat

Yes. What you’re describing is usually called LLM/AI brand monitoring or AI visibility monitoring: you want to know when each assistant mentions your brand, in what context, and whether the mention is positive/neutral/negative.

My top pick: Scrunch

Scrunch is probably the closest fit to your exact requirement.

It lets you:

• Track your brand across ChatGPT, Claude, Gemini, Perplexity, Google AI Overviews/AI Mode, Copilot, Grok, and Meta AI. Scrunch
• See sentiment by AI platform, rather than just one aggregate sentiment score.
• Drill into the individual prompts and actual AI responses behind the score.
• Track brand presence, position, competitor mentions, citations, and sentiment over time. Scrunch Scrunch
• Segment results by things like topic, persona, funnel stage, and branded vs. non-branded prompts. Scrunch

scrunch.com

Other good options

nightwatch.io — tracks mentions and sentiment across ChatGPT, Claude, Gemini and Perplexity. Nightwatch

gumshoe.ai — particularly interesting if you want to compare how the same brand is perceived by different models and buyer personas. Gumshoe AI

answerlift.io — adds factual-accuracy/hallucination monitoring alongside sentiment. AnswerLift

mentionsapi.com — better if you want raw per-model data piped into your own BI/dashboard. It returns brand position, sentiment, context, and cited URLs. MentionsAPI

One important distinction

If your requirement is specifically:

“Show me our brand sentiment separately for GPT vs Claude vs Gemini vs Perplexity, over time, based on a consistent set of prompts.”

I'd shortlist Scrunch and Gumshoe first.

If instead you want:

“Give me an API so we can build our own internal dashboard showing model → prompt → mention → sentiment → competitor → citation.”

I'd look at MentionsAPI.

And if you tell me your brand/category + roughly how many prompts/models you want to monitor, I can narrow this down to the best 2–3 tools and compare pricing, model coverage, sentiment methodology, and API/dashboard capabilities.