ChatGPT answers, unedited

Yes. What you’re describing is usually called AI response evaluation, brand sentiment analysis, AI search/assistant monitoring, or LLM visibility analytics. The exact feature set you want is a bit newer: scoring how AI assistants describe your services, then classifying the user intent behind those prompts (review, recommendation, comparison, alternatives, buying intent, etc.).

A few categories of tools to look at:

Examples:

• Brandwatch and similar social/listening platforms can analyze brand mentions, sentiment, themes, and competitive perception. bigsentiment.com
• Enterpret focuses on combining sentiment scoring with topic detection and customer segmentation. www.enterpret.com
• Chattermill, Thematic, and similar VoC tools are designed to find themes and sentiment patterns across customer language. www.enterpret.com

For your specific requirement, I’d look for these capabilities:

1. Response quality scoring

• Accuracy/factuality
• Brand alignment
• Tone (positive, neutral, negative, cautious, enthusiastic)
• Recommendation strength (“would AI recommend us?”)
• Differentiation vs competitors

2. Intent classification
A useful taxonomy might be:

• Review — “Is this service any good?”
• Recommendation — “What service should I use?”
• Comparison — “Service A vs Service B”
• Alternatives — “What are alternatives to X?”
• Pricing/buying — “How much does it cost?”
• Problem solving — “Who can help me with…”
• Brand validation — “Is X reputable?”

3. AI answer share-of-voice
Metrics like:

• % of prompts where your service appears
• % where you are recommended first
• sentiment of mentions
• competitor mentions
• reasons AI gives for recommending or rejecting you

If you mean “score the answers produced by ChatGPT/Claude/Gemini when users ask about our company”, the closest fit is probably an LLM visibility monitoring platform plus a custom evaluation layer. If you mean “score our own AI assistant’s replies to customers”, then an LLM observability/evaluation tool is the better category.

Some newer customer-feedback platforms also combine sentiment, themes, intent, and entity detection rather than only positive/negative scoring. www.zonkafeedback.com

If you tell me whether you mean monitoring public AI answers about your brand or grading your own chatbot responses, I can narrow it to the right vendors.

Yes. What you're describing is emerging as AI/LLM brand monitoring or AI visibility monitoring—and several tools now get fairly close to that workflow.

The important distinction is that you don't just want to know “does AI mention us?” You want to analyze each assistant response along dimensions like:

• Tone/sentiment: positive, neutral, negative, plus why
• Intent: review, recommendation, comparison, alternative, pricing, “best X,” etc.
• Positioning: what strengths/weaknesses the AI attributes to your service
• Competitive context: who gets recommended instead
• Share of voice / visibility: how often and how prominently you're surfaced
• Evidence: which reviews, websites, citations, or other sources appear to influence the answer

Tools I'd look at

LLM Visibility is particularly close to your description. It explicitly offers sentiment scoring, prompt tracking grouped by buying intent, competitor share of voice, and the raw AI responses behind the metrics. llmvisibility.tech

Saidly is another interesting fit if the tone dimension is central. It queries ChatGPT, Claude, Gemini and Grok, scores responses for sentiment and accuracy, retains the sources, and supports competitor share-of-voice analysis. saidly.ai

CiteShip tracks sentiment, position, recommendations, competitor comparisons and the prompts producing those results. Its examples are explicitly buyer-oriented (“best,” “alternative,” etc.). citeship.com

LLMEyes focuses on AI visibility, competitor recommendations, per-query/per-platform sentiment, and citation-source intelligence. www.indialytics.com

But there's a potentially important gap

If by “segments by intent” you mean something like:

then I don't see a clear off-the-shelf leader that treats intent × tone × competitive positioning as the primary analytical model. Most current products are fundamentally AI visibility trackers with sentiment added, rather than a sophisticated “AI response intelligence” platform.

That could actually be a pretty interesting product category.

A useful output might be something like:

1,240 AI responses analyzed

Recommendation intent: 68% positive
Review intent: 74% positive
Comparison intent: 51% positive
Alternative intent: 43% positive

Primary positive themes: reliability, ease of use
Primary negative themes: price, support
Competitor winning comparisons: X, Y
Most common AI objection: “…”

And crucially, let the user drill down to the exact response that generated each score.

If you're asking because you're evaluating whether to build this internally vs. buy a tool, I can also map the current landscape and identify which tools have APIs/export capabilities and which ones actually support custom intent taxonomies.

Yes. What you’re describing is essentially AI-search / LLM brand monitoring, but with a more granular response-quality + intent analysis layer.

A few tools are particularly close:

• getmint.ai — tracks how ChatGPT, Gemini, Perplexity, Claude, and Google AI Overviews mention/recommend a brand, including sentiment, share of voice, competitors, and intent filters. This is probably the closest off-the-shelf match. GetMint
• frictionai.co — more focused on whether AI actually recommends you, with scores around visibility, sentiment, recognition, and purchase intent. friction AI
• tryreveal.io — analyzes the actual AI responses for tone/framing, e.g. positive vs. negative vs. neutral descriptions, across multiple models. Reveal
• checkaimentions.com — explicitly categorizes prompts by discovery, comparison, and purchase intent and provides sentiment per response. Check AI Mentions

The distinction I'd make

If your goal is specifically:

“When an AI assistant talks about our services, what tone does it use, and does that change depending on whether the user is asking for a review, recommendation, or comparison?”

then I'd want a system that scores each response, not merely overall brand sentiment.

For example:

You could then get something like:

Service A — 1,000 AI responses

• Recommendation score: 78/100
• Tone: 82% positive
• Review intent: 71% positive
• Comparison intent: 64% positive
• Purchase intent: 59% positive
• Most common positive framing: “easy to use,” “reliable”
• Most common negative framing: “expensive,” “limited integrations”
• Competitor most frequently preferred: Competitor X

That's a little different from conventional AI-visibility platforms, which tend to emphasize mention rate, citations, share of voice, sentiment, and recommendation rate. Capterra TechnologyAdvice

If you're evaluating this as a product opportunity: I think there is room for a tool positioned specifically as “AI response intelligence” rather than another AI-visibility tracker. The interesting unit of analysis is the individual answer + user intent + brand framing, with aggregate scores built from that dataset.

If you tell me whether you're looking for an existing SaaS to buy or a tool/API you could use to build this, I can narrow it down considerably.

That's a little different from conventional AI-visibility platforms, which tend to emphasize mention rate, citations, share of voice, sentiment, and recommendation rate.

Yes. What you're describing is essentially an AI-response evaluation / brand-perception monitoring stack with two layers:

1. Intent segmentation — classify the user prompt as review, recommendation, comparison, etc.
2. Response scoring — score the assistant's answer for tone, sentiment toward your brand, accuracy, positioning, recommendation strength, competitive bias, etc.

There are tools that cover pieces of this, but I don't see a single off-the-shelf product that cleanly does all of those dimensions together.

Closest fits

• Future AGI — has an LLM-as-judge tone evaluator that scores the dominant emotional tone of generated responses. docs.futureagi.com
• Noveum — has a ToneConsistencyScorer specifically for evaluating whether an AI response's tone is appropriate and consistent with the conversation/domain. noveum.ai
• MLflow — lets you create custom LLM judges/guidelines, so you could define things like "brand-positive," "accurately represents our service," or "appropriately handles comparison questions." mlflow.org
• AWS Bedrock model evaluation — includes built-in evaluation dimensions such as helpfulness and professional style/tone, with configurable evaluation prompts. docs.aws.amazon.comdocs.cloud.google.com
• Tandem — evaluates AI-agent interactions across multiple behavioral dimensions and can score transcripts, though its taxonomy isn't specifically designed around review/recommendation/comparison intent. tandemscore.com
• Persipica Pulse is interesting if your real objective is "How are AI assistants talking about/recommending our brand?" It tracks AI responses, sources cited, competitors, and head-to-head recommendations. persipica.com

If this is for monitoring your services specifically

I'd structure the evaluation dataset something like:

Then you could answer questions like:

When users ask ChatGPT for a recommendation in our category, how often does it recommend us, what tone does it use when describing us, and how does that differ from comparison queries?

That's a much more useful metric than generic "AI sentiment."

And there's actually a meaningful distinction between sentiment toward your brand and tone of the response. A response can be professional/neutral in tone while being strongly negative about your service. Google, for example, treats sentiment as a numerical score, while LLM-evaluation systems can separately judge tone/style. docs.aws.amazon.comdocs.cloud.google.com

If you tell me what you're trying to monitor (e.g. ChatGPT/Claude/Gemini answers about a SaaS, healthcare service, financial product, etc.), I can find the 3–5 closest products and compare exactly what they can score/segment, including whether they ingest real AI answers automatically.

Yes. What you’re describing is now a fairly well-defined category: AI/LLM brand visibility and response monitoring.

A few tools look particularly close:

• Hootsuite LLM Insights — monitors how AI assistants describe, recommend, and compare your brand, including sentiment and competitor positioning. www.hootsuite.com
• Airbais LLM Evaluator — explicitly analyzes brand mentions by intent/context, including recommendations, comparisons, examples, and explanations, alongside sentiment. airbais.comshubhq.comthreelayerapproach.com
• Presenc AI — tracks AI responses across multiple assistants and analyzes mention position, sentiment, accuracy, citations, and several intent categories such as recommendation, comparison, and problem/solution. presenc.ai
• ShubHQ — has prompt templates for recommendation and comparison, plus visibility, ranking, and sentiment scoring. shubhq.com
• 3LA.ai Brand Analyzer — lets you run customer-relevant prompts across multiple LLMs and track mention rate, sentiment, citations, competitors, and share of AI voice. threelayerapproach.com

The interesting gap

If by "scores the tone" you mean something more sophisticated than positive / neutral / negative, there's still room here.

For example, you could score each assistant response on:

That would let you answer something much more useful than "Does ChatGPT mention us?":

When someone is evaluating our service, how does the AI actually portray us, and is that portrayal helping or hurting us?

That's an important distinction because a mention isn't necessarily a recommendation. Some current AI-visibility research is explicitly moving toward recommendation quality and intent-weighted metrics rather than raw mention counts. llmauthorityindex.com

If you're thinking about building this internally or evaluating vendors, I can also map out the current tools against your exact requirements—e.g. response-level tone scoring + intent segmentation + competitor comparison + historical tracking—and tell you which ones actually do all four.

Yes. There’s a growing category of AI brand / answer-engine monitoring tools that does almost exactly this.

The closest matches I found:

• LLM Pulse — captures actual AI answers from ChatGPT, Perplexity, Gemini, Google AI Overviews/AI Mode, etc., then scores sentiment at the response level. It also lets you inspect the full answer and citations. llmpulse.aisellm.io
• Airbais LLM Evaluator — particularly relevant to your intent segmentation requirement. It explicitly classifies mentions into contexts such as recommendation, comparison, example, and explanation, alongside sentiment. airbais.com
• Profound — tracks how AI platforms describe a brand, including sentiment, recurring themes/attributes, and the sources influencing that framing. help.tryprofound.com
• Pi Datametrics AI Brand Sentiment — tracks positive/neutral/negative framing and connects sentiment to the citations behind the AI response. pi-datametrics.com
• MentionsAPI — interesting if you're thinking of building this internally. It provides structured brand mentions, position, sentiment, competitors, and raw responses across multiple LLMs through an API. mentionsapi.com

For your specific use case

If you're trying to answer:

“When someone asks an AI assistant about our services, what is the user's intent, how does the AI frame us, and how does that compare with competitors?”

I'd structure the measurement as:

The interesting distinction is that plain sentiment isn't really enough. An answer saying “Company X is frequently mentioned, but I wouldn't recommend it for enterprise customers” could technically contain a neutral/positive mention while being commercially negative.

Some newer tools are moving in this direction. For example, Sellm explicitly scores dimensions such as trustworthiness, authority, recommendation strength, and fit for query intent, rather than treating sentiment as a single number. sellm.io

If you're evaluating vendors for a product/service, I'd shortlist Airbais, LLM Pulse, Profound, and MentionsAPI first. Airbais looks closest to your intent × tone requirement; MentionsAPI is more interesting if you want to build your own analytics layer.

If you tell me what “our services” are and whether you want a SaaS tool or an API you can plug into your own pipeline, I can compare the best options specifically on review/recommendation/comparison intent detection, tone scoring, competitor benchmarking, and pricing.

Yes. What you’re describing is essentially AI/LLM brand monitoring + intent classification, and there are now several tools that get pretty close.

The closest matches I found:

• Trustable — probably closest to your exact concept. It queries multiple AI platforms with buying-intent prompts and produces a 0–100 score incorporating visibility, recommendation rate, sentiment, citation depth, and competitive share of voice. www.trustablelabs.com
• MentionGEO — tracks how ChatGPT, Claude, Gemini, Perplexity, etc. describe/recommend a brand, including sentiment, competitor comparisons, and prompt-level performance. www.trustablelabs.commentiongeo.aiwww.getaiso.com
• Aiso — particularly focused on ChatGPT, with brand mentions, sentiment shifts, citations, competitors, and recommendation-oriented prompt sets. www.getaiso.com
• AirOps — more enterprise/AEO-oriented; tracks mentions, sentiment, competitive positioning, citations, and prompt performance across AI answer engines. www.airops.com
• HubSpot AEO — tracks AI visibility, sentiment, share of voice, and prioritized actions. blog.hubspot.com

But there’s an interesting gap

Most of these tools seem optimized around “How visible/favorable is my brand in AI search?”

Your formulation is slightly different:

Given an AI assistant response about our service, score the tone toward us and classify what the user was trying to accomplish.

For example:

You could then create a Response Tone Score—say 0–100—based on dimensions such as:

Positive/negative stance + recommendation strength + prominence + factual accuracy + competitive advantage + confidence.

There is evidence that this kind of structured analysis is already emerging: one 2026 AI-visibility study, for example, explicitly categorized queries by recommendation, comparison/evaluation, local discovery, and informational/advisory intent, then scored responses on citation presence, position, accuracy, sentiment, and actionable information. agentvisibility.ai

So if you're asking “Does a product already exist that gives us a dashboard of how AI assistants talk about our services, segmented by Review vs Recommendation vs Comparison, with a tone score?”:

Yes, partially. But I don't see an obvious market leader whose core product is exactly that combination. The intent taxonomy + response-level tone scoring could be a meaningful differentiation from generic AI visibility monitoring.

If you're evaluating this as a product idea, I can also map out the existing vendors and identify the specific whitespace they aren't covering.

Yes. What you’re describing is emerging as AI brand monitoring / LLM reputation monitoring, but there’s an important distinction: some tools only track whether your brand appears, while others analyze the tone, framing, and intent of the AI answer.

A few that look particularly relevant:

• Qwairy — probably closest to your exact requirement. It says it scores sentiment/tone in AI responses and tracks recommendations, competitive comparisons, perception attributes, and narrative framing across ChatGPT, Claude, Gemini, Perplexity, and others. www.qwairy.co
• CiteShip — tracks AI answers and provides sentiment, competitive comparison, recommendation/share-of-voice data, and the prompts where competitors appear instead of you. citeship.com
• Promptwatch — focused specifically on monitoring brand sentiment inside AI platforms, including tone/framing and competitive positioning. promptwatch.com
• LLM Pulse — tracks AI-generated brand mentions, sentiment, citations, and competitive framing across AI assistants. llmpulse.ai
• Profound / Spotlight / Peec AI — stronger choices if your priority is systematic prompt tracking and AI-search visibility rather than nuanced tone classification. www.prismnews.com

For your use case

I would define the dataset roughly like:

The particularly interesting part is intent segmentation. A brand could have 90% positive sentiment overall but perform poorly on high-commercial-intent prompts like “best X,” “X vs Y,” or “alternatives to X.” Most generic sentiment-monitoring products aren't necessarily designed around that distinction.

If you're evaluating this as a product opportunity, I’d say there is still room beyond existing AI-brand-monitoring tools: “What does the assistant say about us?” → “What kind of buyer question triggered that answer?” → “How persuasive was the answer?” is a more useful analytical layer than simple sentiment.

If you tell me what your services are and whether you're monitoring ChatGPT responses, your own AI assistant, or both, I can map out the existing tools and identify the gap very specifically.

Yes. What you’re describing is increasingly a category of AI-search / LLM brand monitoring, but there’s an important distinction: most tools are good at sentiment/tone + visibility, while fewer natively classify responses by user intent such as review, recommendation, comparison, alternatives, “best X,” pricing, etc.

Closest fits

• Profound — probably the closest overall. It captures AI responses from ChatGPT, Gemini, Perplexity, Claude and others, scores brand sentiment, identifies recurring themes, and lets you organize prompts with tags/topics. help.tryprofound.com

Intent segmentation: You can accomplish it with custom prompt sets/tags, but I wouldn't characterize its core product as an automatic “review vs recommendation vs comparison” classifier.

• Otterly.AI — tracks AI-search responses and sentiment, with prompt-level monitoring and competitor analysis. It's more of an AI visibility/rank tracker than a sophisticated response-intelligence system. technologyadvice.comwww.datadab.com

• AthenaHQ — interesting if your main question is “How is AI describing us?” rather than simply “Are we mentioned?” It focuses on brand representation, framing, sentiment and context. technologyadvice.comwww.datadab.com

• Promptwatch — particularly relevant for the sentiment side. Its positioning is explicitly around monitoring how AI describes brands in recommendation-type responses and analyzing tone/positioning across LLMs. promptwatch.com

If your exact requirement is:

Collect thousands of AI assistant responses about our services → identify whether the user intent was review/recommendation/comparison/etc. → score the tone of the response → break it down by service, competitor, model, and intent

I'd say there isn't an obvious off-the-shelf product that nails all of that in one workflow.

The architecture I'd look for is:

Profound is probably the best starting point if you want to buy rather than build. Its current product already captures the underlying AI answers, sentiment, themes, competitors and prompts, so the remaining gap is largely the custom intent taxonomy. help.tryprofound.com

If you're asking because you want to build this capability for your own company, that's a somewhat different question—and I can lay out the current tools/APIs for collecting the responses and a practical schema for scoring intent × tone × recommendation strength.

Yes. What you’re describing is emerging as AI brand/LLM response monitoring, but there are a few tools that get unusually close to your exact use case.

Closest matches

• Kime — probably the closest match if the key requirement is scoring the tone/perception of AI responses. It scores responses across dimensions including language tone, competitive position, endorsements, source credibility, recency, risk, and confidence—not just positive/negative sentiment. kime.ai

• CiteShip — tracks AI mentions, sentiment, competitor comparisons, and the prompts where competitors appear but you don't. It explicitly scores the tone of mentions. citeship.com

• Persipica Pulse — particularly interesting for your intent segmentation requirement. It breaks prompts into buyer-question types such as research, comparison, and ready-to-buy, and reports recommendations across 12 AI assistants. persipica.com

• Primacy — has a more formal scoring framework covering presence, citation, prominence, share of voice, and sentiment. It also structures its benchmark around awareness, comparison, and branded/buying stages. primacyscore.com

The distinction I'd make

If your desired dataset looks something like:

I don't see a single mainstream product that perfectly nails all of those dimensions out of the box. The market is split between:

1. AI visibility monitoring — Are we mentioned/recommended?
2. Sentiment/perception scoring — How does the AI describe us?
3. Intent/prompt classification — What kind of buyer question produced the response?
4. Agent/LLM evaluation — How good was the assistant's response?

For example, tools such as QEval are much more focused on evaluating your own assistant's conversations—brand voice, empathy, resolution, sentiment, etc.—rather than measuring how external AI assistants talk about your services. qeval.ai

So if you're evaluating a product opportunity, I think there's still a pretty interesting gap: “AI Reputation Intelligence” that takes thousands of assistant responses, automatically classifies each by buyer intent, scores tone/perception, extracts claims and objections, and lets you compare those scores by service, competitor, model, and intent.

If that's what you're exploring, I can also map out the existing vendors + exactly what each does/not does, including which ones expose APIs or raw response data.

Yes. What you're describing is emerging as AI brand visibility / AI search monitoring, but your specific requirement—score the tone of assistant responses and segment responses by user intent—is a little more specialized.

A few tools worth looking at:

• Semrush AI Visibility — tracks how ChatGPT, Gemini, Perplexity, and Google AI describe a brand, including sentiment, narratives, topics, competitors, and visibility. Probably the closest mainstream option. www.semrush.com
• Rankr — scores visibility and sentiment, compares competitors, and tracks results over time. rankr.so
• CitedSpy — runs actual prompts across several AI engines and provides visibility, competitor share-of-voice, sentiment, rankings, and cited sources. www.citedspy.com
• AI Brand Report — provides sentiment, competitor comparisons, citations, and AI-generated prompt suggestions. aibrandreport.com

But your use case is more interesting

I'd structure the analysis as a prompt/response intelligence layer, with each AI answer classified along two dimensions:

That last point is important: AI responses aren't deterministic. Research on measuring AI-search visibility specifically argues that repeated measurements are needed rather than treating one response as a definitive score. arxiv.org

So if you're asking "Is there already a tool that does exactly this?", my answer is:

There are tools that do ~60–80% of it, but I don't see a dominant product whose core abstraction is "AI response sentiment × customer intent."

That could actually be a useful wedge. Instead of another generic "Are you mentioned by ChatGPT?" dashboard, you'd have something closer to:

"How does AI perceive us across the buying journey?"

For example:

• Reviews: "What are customers saying about Service X?"
• Recommendations: "What's the best service for X?"
• Comparisons: "Service X vs. Competitor Y"
• Alternatives: "What are alternatives to X?"
• Purchase intent: "Should I use X?"
• Objections: "What are the downsides of X?"

Then report something like Recommendation Sentiment = 82, Comparison Sentiment = 61, Review Sentiment = 74, with the underlying responses and recurring narratives.

If you're evaluating this as a product/category opportunity, I can also map the existing vendors against those exact capabilities and identify the gap.

Yes. This has become a distinct category often called AI search monitoring, AI brand monitoring, or Answer Engine Optimization (AEO) platforms. Several tools do most of what you're describing, though few expose exactly the same dimensions.

If your goal is:

"Score how AI assistants talk about our services, then segment responses by user intent (reviews, recommendations, comparisons, alternatives, pricing, etc.)"

these are the closest fits:

These platforms monitor responses from ChatGPT, Gemini, Claude, Perplexity, Google AI Overviews, and similar systems, measuring things like:

• mention rate
• recommendation frequency
• share of voice
• citations/sources
• sentiment or answer framing
• competitor mentions
• visibility over time technologyadvice.com

What you're asking that's still uncommon

The interesting part is intent segmentation.

Instead of only asking:

"Was our brand mentioned?"

you want to know:

Then for each intent measure:

• positivity
• recommendation strength
• ranking position
• confidence
• competitors mentioned
• supporting citations

That's more sophisticated than what most commercial dashboards expose today. Most group by prompt or topic rather than explicit buying intent. technologyadvice.com

If you were building this internally

A common pipeline would be:

1. Generate thousands of prompts across buying intents.
2. Query multiple AI assistants (ChatGPT, Claude, Gemini, Perplexity, etc.).
3. Use an LLM to classify each response into intents such as:
   • Review
   • Recommendation
   • Comparison
   • Alternatives
   • Pricing
   • Feature evaluation
   • Risk/compliance
4. Score each response on dimensions like:
   • sentiment (-100 to +100)
   • recommendation strength (1–5)
   • confidence
   • factual accuracy
   • competitor prominence
   • citation quality
5. Build dashboards showing trends by intent, model, geography, and time.

Opportunity

There is still a gap in the market for a platform focused on intent-aware AI response analytics rather than just AI visibility. A dashboard could answer questions like:

• "Our recommendation score is 91, but our comparison score fell 18% this month."
• "We're positive in reviews but lose 65% of 'best X' recommendations."
• "Claude recommends us, but Gemini positions us as an alternative."
• "Negative tone is concentrated in pricing-related prompts."

That kind of analytics is more aligned with product marketing and brand perception than traditional AEO tooling, and relatively few products appear to make intent the primary dimension of analysis.

Yes. This has become a distinct category often called AI search monitoring, AI brand monitoring, or Answer Engine Optimization (AEO) platforms. Several tools do most of what you're describing, though few expose exactly the same dimensions.

If your goal is:

"Score how AI assistants talk about our services, then segment responses by user intent (reviews, recommendations, comparisons, alternatives, pricing, etc.)"

these are the closest fits:

These platforms monitor responses from ChatGPT, Gemini, Claude, Perplexity, Google AI Overviews, and similar systems, measuring things like:

• mention rate
• recommendation frequency
• share of voice
• citations/sources
• sentiment or answer framing
• competitor mentions
• visibility over time technologyadvice.com

What you're asking that's still uncommon

The interesting part is intent segmentation.

Instead of only asking:

"Was our brand mentioned?"

you want to know:

Then for each intent measure:

• positivity
• recommendation strength
• ranking position
• confidence
• competitors mentioned
• supporting citations

That's more sophisticated than what most commercial dashboards expose today. Most group by prompt or topic rather than explicit buying intent. technologyadvice.com

If you were building this internally

A common pipeline would be:

1. Generate thousands of prompts across buying intents.
2. Query multiple AI assistants (ChatGPT, Claude, Gemini, Perplexity, etc.).
3. Use an LLM to classify each response into intents such as:
   • Review
   • Recommendation
   • Comparison
   • Alternatives
   • Pricing
   • Feature evaluation
   • Risk/compliance
4. Score each response on dimensions like:
   • sentiment (-100 to +100)
   • recommendation strength (1–5)
   • confidence
   • factual accuracy
   • competitor prominence
   • citation quality
5. Build dashboards showing trends by intent, model, geography, and time.

Opportunity

There is still a gap in the market for a platform focused on intent-aware AI response analytics rather than just AI visibility. A dashboard could answer questions like:

• "Our recommendation score is 91, but our comparison score fell 18% this month."
• "We're positive in reviews but lose 65% of 'best X' recommendations."
• "Claude recommends us, but Gemini positions us as an alternative."
• "Negative tone is concentrated in pricing-related prompts."

That kind of analytics is more aligned with product marketing and brand perception than traditional AEO tooling, and relatively few products appear to make intent the primary dimension of analysis.

Yes. This has become a distinct category often called AI search monitoring, AI brand monitoring, or Answer Engine Optimization (AEO) platforms. Several tools do most of what you're describing, though few expose exactly the same dimensions.

If your goal is:

"Score how AI assistants talk about our services, then segment responses by user intent (reviews, recommendations, comparisons, alternatives, pricing, etc.)"

these are the closest fits:

These platforms monitor responses from ChatGPT, Gemini, Claude, Perplexity, Google AI Overviews, and similar systems, measuring things like:

• mention rate
• recommendation frequency
• share of voice
• citations/sources
• sentiment or answer framing
• competitor mentions
• visibility over time technologyadvice.com

What you're asking that's still uncommon

The interesting part is intent segmentation.

Instead of only asking:

"Was our brand mentioned?"

you want to know:

Then for each intent measure:

• positivity
• recommendation strength
• ranking position
• confidence
• competitors mentioned
• supporting citations

That's more sophisticated than what most commercial dashboards expose today. Most group by prompt or topic rather than explicit buying intent. technologyadvice.com

If you were building this internally

A common pipeline would be:

1. Generate thousands of prompts across buying intents.
2. Query multiple AI assistants (ChatGPT, Claude, Gemini, Perplexity, etc.).
3. Use an LLM to classify each response into intents such as:
   • Review
   • Recommendation
   • Comparison
   • Alternatives
   • Pricing
   • Feature evaluation
   • Risk/compliance
4. Score each response on dimensions like:
   • sentiment (-100 to +100)
   • recommendation strength (1–5)
   • confidence
   • factual accuracy
   • competitor prominence
   • citation quality
5. Build dashboards showing trends by intent, model, geography, and time.

Opportunity

There is still a gap in the market for a platform focused on intent-aware AI response analytics rather than just AI visibility. A dashboard could answer questions like:

• "Our recommendation score is 91, but our comparison score fell 18% this month."
• "We're positive in reviews but lose 65% of 'best X' recommendations."
• "Claude recommends us, but Gemini positions us as an alternative."
• "Negative tone is concentrated in pricing-related prompts."

That kind of analytics is more aligned with product marketing and brand perception than traditional AEO tooling, and relatively few products appear to make intent the primary dimension of analysis.

Yes. This has become a distinct category often called AI search monitoring, AI brand monitoring, or Answer Engine Optimization (AEO) platforms. Several tools do most of what you're describing, though few expose exactly the same dimensions.

If your goal is:

"Score how AI assistants talk about our services, then segment responses by user intent (reviews, recommendations, comparisons, alternatives, pricing, etc.)"

these are the closest fits:

These platforms monitor responses from ChatGPT, Gemini, Claude, Perplexity, Google AI Overviews, and similar systems, measuring things like:

• mention rate
• recommendation frequency
• share of voice
• citations/sources
• sentiment or answer framing
• competitor mentions
• visibility over time technologyadvice.com

What you're asking that's still uncommon

The interesting part is intent segmentation.

Instead of only asking:

"Was our brand mentioned?"

you want to know:

Then for each intent measure:

• positivity
• recommendation strength
• ranking position
• confidence
• competitors mentioned
• supporting citations

That's more sophisticated than what most commercial dashboards expose today. Most group by prompt or topic rather than explicit buying intent. technologyadvice.com

If you were building this internally

A common pipeline would be:

1. Generate thousands of prompts across buying intents.
2. Query multiple AI assistants (ChatGPT, Claude, Gemini, Perplexity, etc.).
3. Use an LLM to classify each response into intents such as:
   • Review
   • Recommendation
   • Comparison
   • Alternatives
   • Pricing
   • Feature evaluation
   • Risk/compliance
4. Score each response on dimensions like:
   • sentiment (-100 to +100)
   • recommendation strength (1–5)
   • confidence
   • factual accuracy
   • competitor prominence
   • citation quality
5. Build dashboards showing trends by intent, model, geography, and time.

Opportunity

There is still a gap in the market for a platform focused on intent-aware AI response analytics rather than just AI visibility. A dashboard could answer questions like:

• "Our recommendation score is 91, but our comparison score fell 18% this month."
• "We're positive in reviews but lose 65% of 'best X' recommendations."
• "Claude recommends us, but Gemini positions us as an alternative."
• "Negative tone is concentrated in pricing-related prompts."

That kind of analytics is more aligned with product marketing and brand perception than traditional AEO tooling, and relatively few products appear to make intent the primary dimension of analysis.

Yes. This has become a distinct category often called AI search monitoring, AI brand monitoring, or Answer Engine Optimization (AEO) platforms. Several tools do most of what you're describing, though few expose exactly the same dimensions.

If your goal is:

"Score how AI assistants talk about our services, then segment responses by user intent (reviews, recommendations, comparisons, alternatives, pricing, etc.)"

these are the closest fits:

These platforms monitor responses from ChatGPT, Gemini, Claude, Perplexity, Google AI Overviews, and similar systems, measuring things like:

• mention rate
• recommendation frequency
• share of voice
• citations/sources
• sentiment or answer framing
• competitor mentions
• visibility over time technologyadvice.com

What you're asking that's still uncommon

The interesting part is intent segmentation.

Instead of only asking:

"Was our brand mentioned?"

you want to know:

Then for each intent measure:

• positivity
• recommendation strength
• ranking position
• confidence
• competitors mentioned
• supporting citations

That's more sophisticated than what most commercial dashboards expose today. Most group by prompt or topic rather than explicit buying intent. technologyadvice.com

If you were building this internally

A common pipeline would be:

1. Generate thousands of prompts across buying intents.
2. Query multiple AI assistants (ChatGPT, Claude, Gemini, Perplexity, etc.).
3. Use an LLM to classify each response into intents such as:
   • Review
   • Recommendation
   • Comparison
   • Alternatives
   • Pricing
   • Feature evaluation
   • Risk/compliance
4. Score each response on dimensions like:
   • sentiment (-100 to +100)
   • recommendation strength (1–5)
   • confidence
   • factual accuracy
   • competitor prominence
   • citation quality
5. Build dashboards showing trends by intent, model, geography, and time.

Opportunity

There is still a gap in the market for a platform focused on intent-aware AI response analytics rather than just AI visibility. A dashboard could answer questions like:

• "Our recommendation score is 91, but our comparison score fell 18% this month."
• "We're positive in reviews but lose 65% of 'best X' recommendations."
• "Claude recommends us, but Gemini positions us as an alternative."
• "Negative tone is concentrated in pricing-related prompts."

That kind of analytics is more aligned with product marketing and brand perception than traditional AEO tooling, and relatively few products appear to make intent the primary dimension of analysis.

Yes. This category is often called LLM brand monitoring, AI search analytics, or AI answer intelligence. Several tools analyze how AI assistants (ChatGPT, Gemini, Claude, Perplexity, etc.) talk about your brand and competitors, and some also classify responses by user intent.

The capabilities vary quite a bit:

Some well-known vendors include:

• Profound – Focuses on AI search visibility, citations, competitor comparisons, and brand presence across AI assistants.
• Goodie – Tracks how AI assistants recommend brands and products across different prompts and journeys.
• Scrunch AI – Monitors AI search performance and analyzes how brands appear in generative AI responses.
• Otterly.AI – Tracks AI search visibility and changes over time.
• Peec AI – Focuses on AI search rankings and brand visibility.

If your goal is specifically tone analysis by intent, you'll want a platform that either supports custom taxonomies or lets you export responses for analysis.

A common framework looks like this:

Many teams also score each response on dimensions such as:

• Brand favorability (-100 to +100)
• Recommendation strength (1–5)
• Confidence level
• Factual accuracy
• Share of voice
• Mention frequency
• Citation presence
• Competitor preference
• Safety/compliance
• Tone (positive, neutral, negative)

If you have your own prompt corpus, you can also build this yourself:

1. Run thousands of prompts across multiple AI assistants.
2. Store every response.
3. Use an LLM or classifier to label:
   • Intent
   • Tone
   • Sentiment
   • Competitors mentioned
   • Recommendation outcome
   • Key themes
4. Aggregate into dashboards showing trends by product, market, and intent.

This custom approach is often more flexible than off-the-shelf tools because you can define domain-specific intents (for example, "enterprise evaluation," "vendor replacement," or "best CRM for healthcare") and create scoring rubrics tailored to your services.

If you're evaluating vendors, it would help to know:

• Are you monitoring your own brand, clients' brands (agency use case), or many companies at scale?
• Do you want analysis across ChatGPT only, or also Gemini, Claude, Perplexity, AI Overviews, and other AI search experiences?

Yes. This has become a distinct category often called AI search monitoring, AI brand monitoring, or Answer Engine Optimization (AEO) platforms. Several tools do most of what you're describing, though few expose exactly the same dimensions.

If your goal is:

"Score how AI assistants talk about our services, then segment responses by user intent (reviews, recommendations, comparisons, alternatives, pricing, etc.)"

these are the closest fits:

These platforms monitor responses from ChatGPT, Gemini, Claude, Perplexity, Google AI Overviews, and similar systems, measuring things like:

• mention rate
• recommendation frequency
• share of voice
• citations/sources
• sentiment or answer framing
• competitor mentions
• visibility over time technologyadvice.com

What you're asking that's still uncommon

The interesting part is intent segmentation.

Instead of only asking:

"Was our brand mentioned?"

you want to know:

Then for each intent measure:

• positivity
• recommendation strength
• ranking position
• confidence
• competitors mentioned
• supporting citations

That's more sophisticated than what most commercial dashboards expose today. Most group by prompt or topic rather than explicit buying intent. technologyadvice.com

If you were building this internally

A common pipeline would be:

1. Generate thousands of prompts across buying intents.
2. Query multiple AI assistants (ChatGPT, Claude, Gemini, Perplexity, etc.).
3. Use an LLM to classify each response into intents such as:
   • Review
   • Recommendation
   • Comparison
   • Alternatives
   • Pricing
   • Feature evaluation
   • Risk/compliance
4. Score each response on dimensions like:
   • sentiment (-100 to +100)
   • recommendation strength (1–5)
   • confidence
   • factual accuracy
   • competitor prominence
   • citation quality
5. Build dashboards showing trends by intent, model, geography, and time.

Opportunity

There is still a gap in the market for a platform focused on intent-aware AI response analytics rather than just AI visibility. A dashboard could answer questions like:

• "Our recommendation score is 91, but our comparison score fell 18% this month."
• "We're positive in reviews but lose 65% of 'best X' recommendations."
• "Claude recommends us, but Gemini positions us as an alternative."
• "Negative tone is concentrated in pricing-related prompts."

That kind of analytics is more aligned with product marketing and brand perception than traditional AEO tooling, and relatively few products appear to make intent the primary dimension of analysis.

Yes. This category is often called LLM brand monitoring, AI search analytics, or AI answer intelligence. Several tools analyze how AI assistants (ChatGPT, Gemini, Claude, Perplexity, etc.) talk about your company and can break results down by user intent.

Here are some of the stronger options:

If you specifically want response scoring

The most useful systems score dimensions like:

• Mention rate — Are you included?
• Share of voice — How often versus competitors?
• Ranking/position — Are you recommended first, second, etc.?
• Sentiment/tone — Positive, neutral, negative
• Recommendation strength — Strong recommendation vs. mentioned as an option
• Accuracy — Are facts about your products correct?
• Intent category
  • Informational
  • Comparison
  • Recommendation
  • Review
  • Pricing
  • Alternatives
  • Troubleshooting
  • Purchase
• Audience/segment
  • SMB
  • Enterprise
  • Developers
  • Healthcare
  • Finance
  • etc.

The better platforms let you filter by prompts such as:

"Best CRM for healthcare"

versus

"HubSpot vs Salesforce"

versus

"Is HubSpot worth it?"

and show how your visibility and tone differ across those intents.

If you want more control

Many companies build their own evaluation pipeline using an LLM-as-a-judge. The workflow typically looks like this:

1. Collect prompts from real customer journeys.
2. Run them against multiple AI assistants.
3. Use an evaluation model to score each response on:
   • Overall sentiment (-2 to +2)
   • Recommendation strength (0–5)
   • Accuracy
   • Presence of key messages
   • Competitive positioning
   • User intent
4. Store the results in a data warehouse or BI tool.
5. Track trends over time with dashboards.

This approach is common because it lets you define custom intents and evaluation criteria that align with your business rather than relying on a vendor's fixed taxonomy.

If your goal is product analytics

If you're trying to answer questions like:

• "How positive is ChatGPT about our product for enterprise buyers?"
• "Do comparison prompts produce worse outcomes than recommendation prompts?"
• "Which competitors are recommended more often for SMB?"
• "How has our recommendation rate changed over the last month?"

then a custom evaluation pipeline often provides the most flexibility. It can produce a dataset like:

That level of structured scoring is useful for tracking performance over time and identifying which intents or customer segments need better content or messaging.

Yes. There are a few emerging categories of tools that do parts of this, but I don't know of a widely adopted platform that does all three together (tone scoring + brand/service evaluation + intent segmentation) out of the box.

Here's how the landscape breaks down:

For your use case, I'd expect a system that works like this:

1. Run hundreds or thousands of prompts across ChatGPT, Claude, Gemini, Perplexity, etc.
2. Classify each prompt into intents such as:
   • Review
   • Recommendation
   • Comparison
   • Pricing
   • Alternatives
   • Troubleshooting
   • Best for...
3. Score each AI response on dimensions like:
   • Overall sentiment toward your company
   • Confidence
   • Recommendation strength
   • Competitive positioning
   • Accuracy
   • Brand safety
   • Mention frequency
4. Aggregate by:
   • Service line
   • Customer segment
   • Geography
   • AI model
   • Intent

That produces dashboards such as:

This kind of analysis is becoming more common in the "AI search optimization" (sometimes called GEO, or Generative Engine Optimization) space. Products like Persipica Pulse focus on tracking how often AI assistants mention or recommend your brand across different prompt categories, though they tend to emphasize visibility over nuanced tone analysis. persipica.com

If you wanted a more sophisticated solution, it's straightforward to build using an LLM-as-a-judge approach:

• Use an intent classifier (or GPT-5.5) to label prompts.
• Use another model to score responses on a rubric (tone, recommendation strength, factuality, competitor framing, etc.).
• Store results in a warehouse.
• Visualize trends in Tableau, Looker, or Power BI.

This is the approach many teams building AI evaluation platforms are taking because it allows the scoring rubric to be customized to a company's products and brand goals. tandemscore.comarxiv.org

If your goal is specifically measuring how AI assistants talk about your services across customer intents, I'd lean toward a custom evaluation pipeline rather than an off-the-shelf product. The underlying components exist, but tailoring the taxonomy and scoring to your services is usually what makes the insights actionable.

Yes. There are a few categories of tools that do parts of this well, but I don't know of a mature off-the-shelf product that is purpose-built to answer:

"How positive or negative are AI assistants about our services, broken down by user intent (reviews, recommendations, comparisons, alternatives, etc.)?"

Most teams build this from LLM observability + custom evaluation.

Here are the closest options:

What most companies actually build

A typical pipeline looks like:

1. Generate thousands of prompts
   • "Best CRM for startups"
   • "Should I use X or Y?"
   • "Reviews of X"
   • "Alternatives to X"
   • "Is X worth it?"

2. Classify the prompt intent
   • Review
   • Recommendation
   • Comparison
   • Alternative
   • Purchase
   • Troubleshooting
   • Research

3. Run those prompts across ChatGPT, Claude, Gemini, Perplexity, etc.

4. Score every response on dimensions like:
   • Brand mentioned?
   • Position in ranking
   • Overall sentiment (-100 to +100)
   • Confidence
   • Recommendation strength
   • Competitor preference
   • Factual accuracy
   • Source citations

5. Build dashboards such as:

You can also compute metrics like:

• Recommendation Score
• Preference Score
• Competitive Win Rate
• Citation Frequency
• Share of Voice by intent
• Tone consistency
• Purchase likelihood

If you're looking for a commercial product

For monitoring how AI assistants talk about your brand, urlPersipica Pulseturn0search7 is one of the closest offerings because it already categorizes prompts (such as research, comparison, and buying), tracks visibility across multiple AI assistants, and stores the actual responses. persipica.com

If you want deeper analytics

If your goal is something like:

"Show me every recommendation response about Company X this week, classify the intent, score tone from -100 to +100, identify which competitors were favored, and compare trends over time,"

I'd recommend building a custom evaluation pipeline using an LLM evaluator. That gives you much richer outputs than current commercial dashboards, including:

• Intent classification
• Tone/sentiment scoring
• Recommendation strength
• Competitive positioning
• Confidence
• Citation quality
• Longitudinal trend analysis

This approach is increasingly common among companies monitoring their visibility in AI assistants, since intent-specific brand perception is still an emerging area with limited off-the-shelf solutions.