ChatGPT answers, unedited

Yes. What you’re describing is now a fairly specific category: LLM/AI-answer monitoring, but your requirements go a step beyond simple “does ChatGPT mention us?” tracking.

You want something that can answer:

When an AI assistant talks about our services, what does it say about us, how positive/negative is the framing, and what kind of buyer question triggered the response?

Tools worth looking at

• Profound — tracks brand presence, citations, competitors, sentiment, and prompts across AI answer engines. It is geared more toward enterprise-grade AI visibility/brand intelligence. TechnologyAdvice GeoHats
• Semrush AI Visibility Toolkit — particularly relevant to your use case because current descriptions include sentiment tracking and query-intent distribution, alongside mentions/citations and competitor research. Exploding Topics
• Otterly.AI — tracks AI answers, brand mentions, competitors, citations and sentiment. More monitoring-oriented and generally positioned toward marketing/SEO teams. Loudmink TechnologyAdvice
• Peec AI — another AI visibility monitor that focuses heavily on prompt-level results, competitors and how brands appear in AI responses. GeoHats
• Promptwatch — specifically focuses on brand sentiment/tone inside AI-generated answers, rather than conventional social sentiment. Promptwatch

The important distinction

There are actually three different measurements you may want:

The third one is especially useful because you can discover things like:

Recommendation queries: 72% positive framing
Comparison queries: 41% positive framing
Review queries: 63% positive framing
Alternative-to-[competitor] queries: 28% positive framing

That gives you a much more actionable picture than an overall “AI sentiment = 68%” number.

And importantly, intent segmentation isn't universal among these platforms. Semrush's current AI visibility offering explicitly mentions query-intent distribution, while other platforms tend to leave more of the categorization to their prompt/project structure. Exploding Topics TechnologyAdvice

If this is for your own service

I'd structure the measurement around a taxonomy like:

• Recommendation: “What are the best X services?”
• Comparison: “X vs Y”
• Alternative: “Alternatives to X”
• Review: “Is X good?”
• Evaluation: “Is X worth it?”
• Pricing: “How much does X cost?”
• Problem/solution: “How can I solve X?”
• Vendor-specific: “What does [your company] offer?”
• Trust/risk: “Is [company] reputable/reliable?”
• Local: “Best X near me”

Then score each response on mention → position → sentiment/tone → attributes → recommendation strength → competitors mentioned → citations/sources.

If you tell me what kind of services you sell, I can also identify which of these tools comes closest to doing exactly this and what you'd likely have to build yourself.

And importantly, intent segmentation isn't universal among these platforms. Semrush's current AI visibility offering explicitly mentions query-intent distribution, while other platforms tend to leave more of the categorization to their prompt/project structure.

If this is for your own service

I'd structure the measurement around a taxonomy like:

Yes. What you’re describing is essentially an AI-answer / LLM visibility monitoring tool with response-level sentiment, tone, and intent classification.

A few current tools are close:

• Sona AI Search Insights — tracks how AI engines describe a brand, including sentiment scoring, topic clusters, prompt-level results, competitors, and citations. It can slice results by prompt topic, engine, region, language, etc. Sona
• Profound — broader enterprise AI-search monitoring; current comparisons describe it as covering AI visibility, citations, sentiment, and prompt-level analysis. TechnologyAdvice DataDab
• friction AI — particularly interesting for your use case because it explicitly breaks AI visibility into visibility, sentiment, and purchase intent. friction AI
• Georion — uses a structured benchmark that separately measures whether a brand is mentioned, cited, recommended, and accurately/positively represented. Georion

But there’s an important distinction

If you mean:

“Give me 10,000 AI-assistant answers about our service, classify each as review / recommendation / comparison / informational / complaint, then score the tone toward our brand and let me segment the results.”

That's more specific than ordinary AI-visibility tracking.

Most existing products appear to start with a predefined prompt set and measure things like:

Prompt → AI response → brand mentioned? → position → sentiment → citations → competitors

rather than giving you a general-purpose response analytics dataset with arbitrary intent taxonomy.

Your desired schema could look something like:

Sona is probably one of the closer off-the-shelf matches, while friction AI is notable if purchase intent is the key segmentation dimension. Sona friction AI

If you're asking because you want to build this internally, that's also quite feasible: capture assistant responses for a controlled prompt panel, then use an LLM classifier to assign intent/tone and store the raw response + structured scores. I can sketch the architecture and a recommended scoring taxonomy.

Yes. What you’re describing is now a fairly well-defined category: AI/LLM brand monitoring or AI visibility/GEO platforms. They query assistants such as ChatGPT, Gemini, Claude, and Perplexity with buyer-style prompts, then score how your brand is described, recommended, ranked, and compared.

A few that look particularly close to your use case:

• mybrandi.ai — probably the closest match if tone/sentiment + intent segmentation are central. It analyzes buyer prompts across competitors, personas, and market segments and scores themes such as trust, price, reliability, service, and performance. Brandi AI
• lolly.com — tracks AI answers for prompts like “best platform for…,” “compare vendors,” and “alternatives to…,” with sentiment and share-of-voice analysis. Lolly
• llmvisibility.tech — explicitly groups prompts by buying intent, then reports presence, position, sentiment, competitors, and sources. LLM Visibility
• visimatrix.com — has prompt libraries organized around comparisons, alternatives, features, budgets, plus sentiment and recommendation tracking. VisiMatrix
• semrush.com — more enterprise/SEO-oriented, but tracks sentiment, narratives, topics, competitors, and the prompts driving positive/negative brand perception. Semrush Semrush

The specific thing I'd look for

If your taxonomy is something like:

The interesting distinction is that most tools can score sentiment, but fewer seem to treat intent as a first-class dimension and then score the tone/positioning within each intent.

For example, the useful output isn't just:

Acme sentiment: +72

It's:

Recommendation intent: +84 tone, #1 recommendation, 62% inclusion
Comparison intent: +31 tone, loses to Competitor A on price
Review intent: +76 tone, consistently praised for reliability
Alternative intent: −12 tone, frequently framed as expensive

That lets you identify where the AI narrative is helping or hurting you rather than collapsing everything into one sentiment score.

If you're evaluating this for your own product/team, I can also map the market into “already does this” vs. “partial” vs. “would require building it yourself” and identify the closest 5–10 vendors.

Yes. There’s now a fairly mature category for this, usually called AI brand monitoring / LLM visibility / GEO monitoring. The important distinction is that some tools only tell you whether your service appears, while others analyze how the assistant describes it—sentiment/tone, recommendation, competitive positioning, citations, etc. Promptwatch Promptwatch

For your specific use case—“score the tone of AI assistant responses about our services and segment the responses by intent such as review, recommendation, comparison”—I’d look at:

• Promptwatch — probably one of the closest fits. It monitors how brands are represented across ChatGPT, Claude, Gemini, Perplexity and other AI surfaces, including sentiment and prompt categories. Promptwatch
• LLM Visibility — explicitly reports sentiment/tone at the answer level and groups prompts by buying intent. It also preserves the underlying answer, so you can inspect why a score was assigned. LLM Visibility
• Profound / AthenaHQ / Scrunch / OtterlyAI — broader AI-search monitoring platforms that track mentions, recommendations, competitors, citations and sentiment. BigSentiment Kairosy
• Avouch — interesting if you care heavily about recommendation behavior. It has a Recommendation Rate alongside sentiment, share of voice and the actual answer evidence. Avouch

But there's a gap

If what you mean is something more specific like:

Give us 10,000 AI answers about our service → automatically classify each as Review / Recommendation / Comparison / How-to / Alternatives / Complaint → score tone, favorability, accuracy, confidence, and positioning → aggregate by intent, competitor, model, and time.

That's not quite the standard product experience of most LLM-monitoring platforms. They tend to start with brand visibility and sentiment, then add prompt/topic segmentation. BigSentiment Promptwatch

You could build that fairly cleanly as an evaluation layer on top of an LLM-monitoring dataset:

That would let you answer much more interesting questions than simply “Is ChatGPT mentioning us?”—for example, “When users ask for recommendations, how favorably does AI position us versus competitors?” or “Our sentiment is positive overall, but does it deteriorate specifically in comparison prompts?”

If you're evaluating this as a product opportunity rather than looking for a tool to buy, I can also map out the existing players and identify the white space for a tool specifically focused on AI-response tone + intent scoring.

Yes. What you’re describing is now generally called AI visibility / AI brand monitoring, but your requirements are a little more specific than basic visibility tracking.

You want to measure:

• Tone / sentiment: Is the assistant describing your service positively, neutrally, or negatively?
• Intent: Is the prompt asking for a review, recommendation, comparison, etc.?
• Positioning: Are you the recommended choice, an alternative, or merely mentioned?
• Competitive context: Who are you being compared against?
• Assistant/model: How does the answer differ across ChatGPT, Gemini, Claude, Perplexity, etc.?

There are tools that get fairly close. Current AI-monitoring platforms increasingly track sentiment, positioning, and prompt-level performance rather than just whether your brand was mentioned. TechnologyAdvice Toolsolved

The ones I'd look at

• Promptwatch — probably closest to your use case if tone/framing + intent + actionable analysis are important. It specifically monitors how AI platforms portray brands and connects sentiment/visibility to optimization. Promptwatch
• Evertune — particularly focused on brand perception, sentiment, framing, and competitive positioning in AI responses. Toolsolved
• Profound — stronger if you want an enterprise-grade AI visibility/analytics platform with sentiment, citations, competitive benchmarking, and prompt analytics. TechnologyAdvice
• Scrunch AI — good for prompt monitoring, competitor benchmarking, citations, and sentiment. TechnologyAdvice Toolsolved
• Diploria — interesting if you specifically want to inspect the full response, rank, citations, and a sentiment label at the prompt level. Diploria AI
• DiscoveredBy — tracks prompts across several major assistants and explicitly provides sentiment plus competitive/share-of-voice analysis. DiscoveredBy

But there's an important gap

I don't see a mature off-the-shelf product that perfectly matches:

"Take all AI assistant responses about our services → classify each prompt by buyer intent → score the response's tone toward us → segment results by intent → benchmark against competitors."

Most products start with prompt portfolios that you define, rather than automatically discovering and reliably classifying every possible review vs recommendation vs comparison interaction. Intent segmentation is therefore something I'd verify carefully in demos.

If you're evaluating this for your own product/team, I can also map out the exact scoring framework I'd use — e.g. Intent × Sentiment × Recommendation Strength × Competitive Position × Accuracy — and tell you which existing tools can cover each column versus where you'd need your own classifier.

Yes. What you’re describing is emerging as AI/LLM brand monitoring or AI visibility monitoring.

The closest tools I found are:

• Profound — tracks how brands appear in AI answers, including sentiment, visibility, competitors, and citations. It’s geared toward enterprise AI-search intelligence. TechnologyAdvice
• HubSpot AEO — particularly interesting if you want to organize prompts by product/customer segment and measure sentiment + competitor share of voice across AI answers. HubSpot Blog
• Promptwatch — specifically focused on how brands are described in AI responses, including sentiment/tone across ChatGPT, Gemini, Perplexity, Claude, etc. Promptwatch
• Peec AI / Otterly.AI / Scrunch / AthenaHQ — more broadly focused on AI visibility, prompt tracking, citations, competitors, and in some cases sentiment/context analysis. TechnologyAdvice Slate

The important distinction

Your use case is slightly more sophisticated than ordinary "AI visibility."

You want something like:

So ideally the system would capture the full AI response, classify the user's intent, score the tone toward your company/services, extract the claims/attributes, and then aggregate those metrics over time.

I don't see a mainstream off-the-shelf product that cleanly advertises that exact combination of intent segmentation + response-level tone scoring + service-level analysis. Most current products emphasize AI visibility, mentions, citations, sentiment, and competitive share of voice. TechnologyAdvice Slate

If you're asking because you want to build this internally, this is actually quite feasible: you could run a prompt corpus against ChatGPT/Claude/Gemini/Perplexity, preserve each response, then use a separate classifier to label intent × sentiment × recommendation strength × competitor mentions × claims. That would give you a much richer dataset than conventional AI-visibility tools.

Yes. What you’re describing is basically AI visibility / AI brand monitoring, but with a more useful layer of intent segmentation + response-quality scoring.

Several tools now get fairly close:

• brandcitation.com — tracks mentions, sentiment, position, share of voice, and citations across AI-generated answers. It explicitly supports buyer-intent prompts such as recommendations and alternatives. BrandCitation
• modelmention.io — lets you build a prompt library and organize buyer questions with strategy tags, then measure visibility, position, sentiment, and competitor overlap. ModelMention
• ahoylighthouse.com — measures how assistants describe/recommend your brand and includes sentiment, citations, competitor benchmarks, and a visibility score. Lighthouse
• gumshoe.ai — particularly interesting if you want to evaluate how AI talks about you. It runs buyer-persona conversations across multiple models and compares recommendation rates and sentiment. Gumshoe AI
• letaeo.com — goes beyond simple mentions by distinguishing considered → shortlisted → recommended → first choice, which is quite close to the intent/outcome framework you're describing. Letaeo

But there's an important gap

I don't see a mainstream tool that cleanly does exactly:

Run thousands of AI-assistant responses about our services → classify each response by user intent → score the tone/quality of the assistant's representation of us → aggregate by intent.

For example:

Then you could create something like an AI Response Score:

Overall score = 40% recommendation + 25% sentiment + 20% factual accuracy + 15% competitive position

…and slice it by intent × model × competitor × time.

That's more sophisticated than conventional "AI visibility" because you're measuring the quality of the AI's representation of the brand, not merely whether the brand appeared.

If you're considering building this as a product/internal analytics system, I can also map out what the scoring schema, prompt taxonomy, and dashboard would look like—and which existing tools you'd be competing with.

Yes. There’s now a fairly distinct category for this: AI brand perception / AI-search sentiment monitoring.

For what you described, I’d look at these:

• frictionai.co — probably the closest match. It scores how AI portrays a brand, including sentiment, recommendation status, reservations, and the underlying response. It can segment by buying context and compare ChatGPT, Gemini, Claude, Perplexity, and Google AI Overviews. friction AI
• gumshoe.ai — focuses on why AI recommends a product: themes, attributes, product associations, and competing products appearing alongside yours. Gumshoe
• centium.ai — captures the actual language AI uses about your brand, clusters descriptors into themes, and scores their sentiment. It also records prompt category and recommendation context. Centium
• prompts-gpt.com — more focused on ChatGPT monitoring: mentions, recommendations, sentiment, position, competitors, and citations. Prompts GPT
• tracemetry.com — explicitly treats AI answers as positive/neutral/negative/mixed and recommends tracking them by prompt/intent. Tracemetry

The important distinction

If your desired dataset is something like:

Yes, tools can get quite close to this. The more sophisticated ones aren't just doing generic sentiment analysis; they're distinguishing mention → favorable framing → conditional recommendation → active recommendation → advised against. friction AI BrandJet AI

And that's important because a simple "sentiment = 82%" score isn't necessarily useful. For example, an AI could say “Company X is expensive but excellent for enterprise buyers”—overall sentiment might be positive, but the intent and qualification tell you much more about whether you're winning the buyer.

If you're evaluating this for your own product/company, I can also give you a shortlist of the 3–5 best tools specifically for measuring AI assistant perception across review vs recommendation vs comparison prompts, including what each actually scores and approximate pricing.

Yes. What you’re describing is basically AI answer/visibility monitoring + response-level sentiment/tone + intent classification.

The closest existing category is AI visibility / AEO / GEO monitoring. These tools run buyer-style prompts against ChatGPT, Claude, Gemini, Perplexity, etc., then analyze whether your brand appears, how it is described, competitors mentioned, and often sentiment/tone. Refine AI Zerply

A few worth looking at:

• Refine — tracks AI responses across multiple assistants and reports citation rate, share of voice, sentiment, and sources. Refine AI
• Profound — more enterprise-oriented AI visibility/brand intelligence; useful if you want broad monitoring and competitive analysis. LLM Visibility Tools
• Peec AI — focused on granular AI-answer/mention analysis, including sentiment and competitive visibility. Peekl
• Otterly AI — lighter-weight ongoing monitoring of how brands appear in AI answers. Cruelx

But your specific use case is a little different

If your goal is something like:

"When an AI assistant talks about our services, what tone does it use, and what kind of buyer question triggered the response?"

you could structure the analysis as:

That intent × tone × recommendation-strength layer is more specific than ordinary AI visibility monitoring. Most existing tools seem stronger at "Are we mentioned and how do we compare?" than at building a detailed taxonomy of why the assistant is talking about you and what kind of buyer intent produced the answer. Peekl Refine AI

If you're evaluating this as a product idea, I think there's an interesting gap here: rather than another "AI visibility score," you could build a Response Intelligence system that samples thousands of assistant answers and tells a services company "AI recommends you strongly for X, cautiously for Y, and negatively frames you for Z."

Yes. The category you’re describing is emerging as AI answer monitoring / AI brand perception analytics (sometimes grouped with AEO/GEO tools), rather than traditional sentiment analysis. The ideal tool would:

1. Collect AI assistant responses (ChatGPT, Gemini, Claude, Perplexity, AI Overviews, etc.) for a set of prompts about your services.
2. Score tone/sentiment (positive, neutral, negative, confidence, trust, recommendation strength).
3. Classify user intent (e.g., review, recommendation, comparison, alternatives, pricing, “best provider,” complaint).
4. Benchmark competitors and show where your brand is favored or omitted.
5. Explain why the AI responded that way (sources/citations, reviews, content gaps).

A few tools in this space:

• promptwatch.com — focused on monitoring how brands appear in AI-generated answers, including sentiment/framing across AI platforms. Promptwatch
• tryprofound.com — tracks AI search visibility, mentions, citations, and sentiment-related brand insights. TechnologyAdvice
• scrunch.ai — focuses on AI visibility, citations, competitor benchmarking, and how AI systems represent brands. Kairosy
• otterly.ai — tracks prompts and AI search visibility across engines. Kairosy

If your main goal is “how are AI assistants describing our company when customers ask buying questions?”, I’d look at the AI visibility tools above.

If your goal is “we have thousands of AI-generated answers/support conversations and want to score tone + intent automatically”, a more general NLP/VoC platform may fit better:

• chattermill.com
• qualtrics.com
• getthematic.com

These are stronger at theme extraction, sentiment, and intent classification over large text datasets. Zonka Feedback

For your specific examples (“review,” “recommendation,” “comparison”), I would define a custom intent taxonomy such as:

Then score each AI answer on:

• Brand mention rate
• Position/ranking
• Sentiment
• Recommendation likelihood
• Key strengths mentioned
• Weaknesses mentioned
• Competitors surfaced
• Sources cited

If you’re building this internally, it’s also very feasible with an LLM evaluation pipeline: store prompts → collect responses → run a classifier with a fixed rubric → dashboard results. For enterprise brand monitoring, the tools above are the closest off-the-shelf options.

Yes. What you’re describing is emerging as AI visibility / LLM brand monitoring, but there’s an important distinction: many tools measure whether AI mentions you and the overall sentiment, while fewer give you a clean intent × tone breakdown.

A few worth looking at:

• finseo.ai — particularly close to your use case. It tracks AI answers across ChatGPT, Claude, Perplexity, Gemini, etc., scores sentiment/framing, and explicitly tracks prompt/buyer intent, including comparisons and “best of” prompts. Finseo
• hubspot.com — scores how AI engines represent a business, with sentiment as a major component and contextual sentiment by topic/use case. Better for an overall brand score than granular intent segmentation. HubSpot
• sona.com — monitors AI responses, sentiment, citations, competitors, prompts, and lets you slice metrics by prompt topic and other dimensions. Sona
• ahoylighthouse.com — strong for measuring how ChatGPT/Claude/Gemini/etc. describe and recommend your brand, with sentiment, citations, competitor positioning, and per-prompt results. Lighthouse
• livesov.com — tracks sentiment per AI mention and supports custom queries, including variations and intents, with the underlying model response available for inspection. Livesov

The specific framework I'd look for

If your goal is something like:

…then Finseo looks closest among the tools I found, because it explicitly combines sentiment/framing with prompt/intent coverage. Finseo

But if you're asking because you're considering building this internally, I think there's still a meaningful product gap: a system that takes thousands of AI-assistant responses and produces a standardized Intent → Positioning → Tone → Recommendation Strength → Competitive Context dataset. Most current products emphasize AI visibility/share of voice rather than deep response-level qualitative scoring.

If you tell me what "our services" are and whether you want to analyze ChatGPT responses, customer reviews, or both, I can map out the best existing tools and what you'd need to build yourself.

Yes. What you’re describing is now a fairly established category: AI/LLM brand monitoring or AI visibility tracking.

The closest tools I found are:

• Promptwatch — specifically focuses on how AI assistants describe brands, including sentiment, across ChatGPT/Gemini and other AI platforms. Promptwatch
• Kairosy — particularly close to your use case: it runs buyer-intent questions, classifies the resulting answer's sentiment, and traces mentions back to the source/review/thread influencing the answer. Kairosy
• ShubHQ — explicitly supports prompt categories such as comparison and recommendation, and gives a 0–100 visibility score plus positive/neutral/negative sentiment. ShubHQ
• DiscoveredBy — monitors ChatGPT, Gemini, Perplexity, Claude, and Grok at the prompt level and tracks what is actually said about your brand. DiscoveredBy
• Profound / Scrunch / Peec / Otterly — broader AI-visibility platforms that track mentions, recommendations, competitors, citations and related metrics. The Answer Engine Report Marketing for LLMs

But there's an important distinction

If your desired dataset is something like:

I don't see a single obvious off-the-shelf product that perfectly owns that entire schema. Most current tools are stronger at visibility/mention/citation tracking than at sophisticated intent classification + response-quality scoring. The category is moving in that direction, though. Ansvisor ONSAAS

In fact, I think there's a potentially interesting product wedge here: "How does AI talk about our services, by buyer intent?" Rather than simply asking "Does ChatGPT mention us?", you'd measure:

• Intent: review / recommendation / comparison / alternatives / pricing / best-of / troubleshooting, etc.
• Tone: positive ↔ negative, confidence, enthusiasm, objections
• Position: recommended / mentioned / excluded
• Comparative outcome: wins / loses / tied
• Attribute-level sentiment: price, quality, reliability, support, features, etc.
• Source influence: which reviews, Reddit threads, publications, pages, etc. appear to drive the answer
• Model variance: ChatGPT vs. Claude vs. Gemini vs. Perplexity
• Trend: whether the AI narrative about your service is improving or deteriorating

That would be substantially more actionable than a generic "AI visibility score."

If you're evaluating this as a product idea, I can also map out the existing competitors, exactly what each one measures, and the gap I'd target.

Yes. What you’re describing is emerging as AI brand/LLM monitoring or AI search visibility monitoring.

A few tools are particularly close:

• Tracemetry — explicitly scores the tone/framing of AI answers as positive, neutral, negative, stale, or misleading, and treats sentiment as a layer on top of visibility. Tracemetry
• Aiso — tracks brand mentions, sentiment shifts, competitors, and how often/where your brand is recommended in ChatGPT answers. Aiso
• CiteShip — monitors ChatGPT, Claude, Gemini, Perplexity, and Grok, including sentiment, recommendation position, competitors, and prompt-level results. CiteShip
• Trakkr — tracks buyer-intent prompts across discovery, comparison, recommendation, and support, alongside sentiment, competitor share, citations, and answer mode. Trakkr
• Presenc AI — its analysis explicitly categorizes prompts into recommendation, comparison, category exploration, best-of, and problem/solution intents, then analyzes sentiment and brand positioning. Presenc AI

The important distinction

If your requirement is specifically:

“Give me every AI assistant response about our services, classify the user's intent, then score how positively/negatively the assistant talks about us.”

that's a little more specific than conventional sentiment monitoring.

I'd want the data model to look something like:

That lets you answer much more useful questions than simply “What's our sentiment?” — for example, “We're highly positive in recommendations but negative in comparisons because AI repeatedly describes us as expensive.”

There are already products doing pieces of this, and some are getting quite close to the full workflow. Dageno AI BigSentiment

If you're asking because you want to build this for your own services, I can also map out what the product would need to collect, how I'd score tone/intent, and which existing tools you'd actually be competing with.

Yes. What you’re describing is usually built from a combination of LLM response evaluation, sentiment/tone analysis, and intent classification rather than a single universal tool.

For your use case — “score how AI assistants talk about our services and segment responses by user intent (review, recommendation, comparison)” — look at these categories:

1. AI answer monitoring / LLM visibility platforms

These are closest if you want to measure how your brand appears in ChatGPT, Claude, Gemini, Perplexity, etc.

• tryprofound.com — tracks how brands appear in AI-generated answers and analyzes visibility/share of voice.
• peec.ai — monitors AI search visibility and competitor mentions.
• scrunchai.com — focuses on tracking brand presence in generative AI answers.

Typical metrics:

• Brand mention rate
• Position/ranking in AI recommendations
• Competitor inclusion
• Sentiment toward your brand
• Citation/source patterns

These are more about “what AI says about us” than “is the AI response tone good?”

2. LLM response evaluation platforms

These score AI outputs on dimensions like helpfulness, tone, accuracy, and alignment.

• LangSmith — lets teams create evaluators for AI responses, including custom LLM judges for tone, brand voice, and intent categories.
• Arize AI — provides LLM evaluation and monitoring workflows.
• Humanloop — supports testing and scoring model outputs.

You can create evaluators like:

3. Customer feedback and sentiment intelligence tools

If you are analyzing many AI-generated answers or conversations at scale:

• Chattermill
• Enterpret
• Thematic

These specialize in combining sentiment with themes/topics. Enterpret

4. Build-your-own pipeline (often the best fit)

Many companies create a lightweight system:

1. Collect AI responses:

• ChatGPT queries
• Claude queries
• Gemini queries
• Perplexity queries
• Run each response through an evaluator model:

Example schema:

{
  "intent": "comparison",
  "tone": "positive",
  "brand_sentiment": 8,
  "recommendation_likelihood": 0.82,
  "competitors_mentioned": ["Competitor A"],
  "key_claims": [
    "best value",
    "easier setup"
  ]
}

1. Dashboard:

• % of answers recommending you
• Average tone score
• Intent distribution: - 40% comparisons
• 35% recommendations
• 25% reviews
• Competitor win/loss rate

For a services company, I’d probably combine an AI visibility tracker (to collect real-world AI answers) with a custom LLM judge (to score tone, intent, and positioning). Existing evaluation tools can score agent behavior and transcripts, but brand-specific intent categories like “recommendation vs comparison vs review” usually require custom evaluators. Tandem

If you mean “monitor how ChatGPT/Claude recommend our company to consumers” specifically, the category is now often called Generative Engine Optimization (GEO) / AI Search Visibility tracking. If you mean “grade our own chatbot’s replies”, the tooling is different.

Yes. What you’re describing is usually called AI response monitoring, LLM response analytics, AI visibility / answer engine optimization (AEO) tracking, or brand sentiment + intent classification. The exact capability—scoring the tone of AI assistant answers about your services and segmenting the answers by user intent (review, recommendation, comparison, etc.)—is emerging rather than a single mature category.

Tools that can help:

Examples of platforms in adjacent categories:

• brandwatch.com — enterprise social/listening analytics; useful for brand perception monitoring. BigSentiment
• chattermill.com — customer feedback analytics with sentiment and theme classification. Enterpret
• qualtrics.com — enterprise experience analytics with sentiment capabilities. Dupple
• aws.amazon.com — API-level sentiment and text classification if you want to build your own pipeline. BrandJet AI

For your specific scenario, I’d define the scoring schema first:

Response-level scores

• Brand sentiment: -100 to +100
• Recommendation strength: 0–100
• Accuracy / factual alignment
• Brand tone match
• Competitor mention score
• Citation/share-of-voice score

Intent categories

• “Is X good?” - Review / opinion intent
• “X vs Y” - Comparison intent
• “Best providers for…” - Recommendation intent
• “How does X work?” - Research intent
• “Where can I buy/use X?” - Transaction intent

A practical architecture is often:

1. Collect AI assistant outputs (ChatGPT, Gemini, Perplexity, Claude, etc.)
2. Store prompts + responses
3. Run an LLM classifier: - intent label
4. sentiment score
5. competitor mentions
6. claims about your services
7. Dashboard trends over time

If you mean “I want to know how ChatGPT/Gemini/Claude recommend my company when users ask about our category”, that is closer to AI brand monitoring / AEO tracking. If you mean “we have our own AI assistant and want to score its replies,” that is closer to LLM evaluation and observability.

Yes. There are tools that cover most of this, but your exact use case—scoring how AI assistants describe your services, then segmenting those responses by intent such as review/recommendation/comparison—is still a somewhat specialized workflow.

A few relevant categories:

• LLM/GEO monitoring: LLM Pulse scores sentiment on AI responses about brands and lets you inspect responses across ChatGPT, Perplexity, Gemini, and Google AI. llmpulse.ai
• AI-search sentiment: Scout LLM tracks positive/neutral/negative framing of brand mentions and aggregates it into a net sentiment score. scoutllm.ai
• LLM evaluation/observability: PostHog AI Evals and tools such as Datadog LLM Observability let you create custom LLM-as-a-judge criteria, including tone, helpfulness, and intent-related evaluations. github.com
• Conversation/AI-agent analytics: Polaryst is closer to your segmentation idea: it supports custom graders for things like tone and intent recognition and lets teams analyze failure rates by intent/cohort. polaryst.com

The workflow I'd use for your specific problem

Rather than just a generic "sentiment score," I'd create a structured evaluation for every AI answer:

Then you can answer questions like:

"When ChatGPT is asked for recommendations in our category, how favorably does it describe us?"

or:

"We're positive in 78% of recommendation answers, but only 41% of comparison answers. What language is causing that?"

That's substantially more useful than ordinary sentiment analysis because "positive" isn't necessarily "recommended." An answer can say wonderful things about your service while still recommending a competitor.

If you're asking because you're considering building this as an internal analytics tool or evaluating vendors for your company, I can also map out the current landscape and identify which tools can actually do review/recommendation/comparison intent segmentation out of the box vs. which would require custom LLM evaluation.

Yes. What you’re describing is now generally called AI visibility / LLM brand monitoring or GEO/AEO monitoring.

Several tools are close to your exact use case:

• mentionscout.com — tracks AI answers across ChatGPT, Claude, Gemini, Perplexity, etc., with sentiment, position, competitor share of voice, and prompt-level analysis. It also explicitly supports prompts like “best X,” “X vs Y,” and “alternatives to X.” MentionScout
• getlatentline.com — particularly close to what you're describing: sentiment + recommendation position + share of voice, with evidence from the actual captured AI response. It also identifies comparison gaps. Latentline
• llmmetrix.com — monitors mentions, sentiment, placement, citations and competitors, and can alert you when sentiment changes. LLM Metrix
• mentiongeo.ai — explicitly focuses on how AI answers recommend, compare and describe brands, including sentiment and competitor tracking. MentionGEO
• clovion.ai — tracks sentiment and connects it to the particular buyer prompts and source context producing the positioning. Clovion

The important distinction

Most of these tools can score something like:

“How positively does the AI describe/recommend our brand?”

and segment by the prompt/query that generated the response.

But if you specifically want a taxonomy like:

—that's less standardized. The products I've found tend to let you create/track prompt sets, rather than giving you a universally reliable “intent classifier” out of the box.

For your use case, I'd actually look for a system that stores the raw assistant response and then applies a second layer of analysis:

Prompt → Intent classification → Brand mention → Sentiment/tone → Recommendation strength → Competitor comparison → Attributes discussed → Evidence/citations

That gives you much more useful reporting than a single “AI sentiment = 78” score.

If you're evaluating this as a product/analytics capability for your own company, I can also map out the existing tools against exactly those dimensions and tell you which ones already do intent segmentation vs. what you'd have to build yourself.

Yes. What you’re describing is essentially AI-answer/LLM brand monitoring, but with a more granular layer of intent segmentation + response tone scoring.

A few tools appear particularly close:

• tryprofound.com — tracks how brands appear in AI answers, including visibility, recommendations, competitors, citations, and sentiment. It’s probably the strongest enterprise-oriented option. TechnologyAdvice
• citeship.com — explicitly scores the tone of AI mentions and tracks competitor recommendations and buyer prompts. This sounds particularly close to your requirement. CiteShip
• tracemetry.com — specifically focuses on AI-search sentiment: whether an assistant describes a brand positively, neutrally, negatively, or with problematic/stale framing. Tracemetry
• search-visibility.ai — scores AI responses from 0–100 and tracks mentions, sentiment, rank, and competitor share across multiple models. AI Search Visibility

The interesting gap

If by "segments by intent" you mean something like:

then most existing products don't seem to make that taxonomy the central unit of analysis. They tend to start with prompts → AI responses → mention/sentiment/visibility/competitor metrics. AirOps Slate

So there may be an opportunity for a more purpose-built product: "AI Response Intelligence" rather than just AI visibility monitoring.

For example, you could score every response on:

Intent → Brand presence → Tone → Position → Competitive framing → Claims → Recommendation strength

and then aggregate:

Recommendation intent: 72% positive
Comparison intent: 48% positive
Review intent: 81% positive
Purchase intent: 64% positive

That would tell a services company how AI assistants are actually selling or undermining them, rather than simply whether they're mentioned.

If you're evaluating this as a product idea, I can also map out the existing vendors and identify the specific whitespace in this market.

Yes. What you’re describing is usually handled by a combination of LLM evaluation tools, conversation analytics platforms, and custom classifiers. I don’t know of a single off-the-shelf product that perfectly does all of:

1. Scores AI assistant response tone against your brand/service guidelines
2. Classifies the user’s intent (review, recommendation, comparison, support, purchase research, etc.)
3. Aggregates results by product/service, audience segment, model, prompt, or channel

But several tools get close:

AI response quality + tone scoring

• Braintrust — Built for evaluating LLM outputs with custom scoring rubrics. You can define graders like “professional tone,” “trustworthiness,” “brand alignment,” or “avoids unsupported claims.”
• Langfuse — Tracks LLM traces and supports evaluation scoring, making it useful for analyzing large volumes of assistant conversations.
• Promptfoo — Good for repeatable evaluations where you create tests such as “Does the answer recommend our service appropriately?” or “Does it compare us fairly?”
• Arize AI — Offers LLM monitoring/evaluation workflows, including quality and behavior analysis.
• Tandem — Focused on grading AI agent behavior across dimensions like usefulness, alignment, and groundedness. Tandem

For tone specifically, there are also LLM-as-a-judge scorers that evaluate whether a response matches a desired style, domain, and conversation context. Noveum.ai

Intent segmentation

For your intent buckets:

• Review → “Is the user evaluating past experience?”
• Recommendation → “Which product/service should I choose?”
• Comparison → “A vs B / alternatives / competitors”
• Research → “What does this service do?”
• Purchase intent → “Pricing, availability, how to buy”
• Objection → “Why is this better than competitor X?”

You can use:

• OpenAI models with structured classification prompts
• Anthropic models similarly
• Traditional NLP classifiers if you need lower cost at high volume

Intent classification is a mature NLP task; many NLU platforms support mapping utterances into custom intent categories. arXiv

A typical setup for a services brand

A practical pipeline would look like:

AI assistant response logs
        ↓
Intent classifier
        ↓
Review / Recommendation / Comparison / Other
        ↓
Tone evaluator
        ↓
Scores:
- Brand alignment (1-10)
- Positive/negative sentiment
- Confidence
- Accuracy / claim risk
- Persuasiveness
        ↓
Dashboard by:
- Service
- Competitor mentioned
- User intent
- AI model
- Prompt version

Example scoring rubric:

If your goal is specifically “how do AI assistants talk about our company?”

I’d look at an LLM visibility / AI search monitoring category as well. Those tools track how brands appear in AI-generated answers and can analyze mentions, recommendations, and competitor comparisons.

If you tell me:

• your industry (SaaS, travel, healthcare, finance, etc.)
• whether you mean ChatGPT/Claude/Gemini public answers or your own chatbot
• volume (hundreds vs millions of responses)

I can suggest a tighter tool stack.

AI assistant response logs
        ↓
Intent classifier
        ↓
Review / Recommendation / Comparison / Other
        ↓
Tone evaluator
        ↓
Scores:
- Brand alignment (1-10)
- Positive/negative sentiment
- Confidence
- Accuracy / claim risk
- Persuasiveness
        ↓
Dashboard by:
- Service
- Competitor mentioned
- User intent
- AI model
- Prompt version

Example scoring rubric:

If your goal is specifically “how do AI assistants talk about our company?”

I’d look at an LLM visibility / AI search monitoring category as well. Those tools track how brands appear in AI-generated answers and can analyze mentions, recommendations, and competitor comparisons.

If you tell me:

• your industry (SaaS, travel, healthcare, finance, etc.)
• whether you mean ChatGPT/Claude/Gemini public answers or your own chatbot
• volume (hundreds vs millions of responses)

I can suggest a tighter tool stack.

Yes. What you’re describing is emerging as AI-answer / LLM brand monitoring, but there’s an important distinction: most tools are better at “what does the AI say about us?” than at systematically classifying why the user asked.

A few worth looking at:

• promptwatch.com — probably closest to your use case. It monitors how ChatGPT, Gemini, Perplexity, Claude, etc. represent a brand and provides sentiment/visibility analysis. It can segment results by prompt categories. Promptwatch
• tryprofound.com — enterprise-oriented AI-search monitoring. Tracks AI answers, citations, competitors, sentiment and prompt-level insights. TechnologyAdvice
• otterly.ai — more focused on AI-search visibility/prompt tracking, with competitive and content analysis. TechnologyAdvice
• peec.ai — tracks how brands appear in AI answers and provides competitive/prompt analysis. Geodocs

But your exact requirement is a little more specialized

If you want something like:

…I don't see a mainstream product that perfectly nails all three layers simultaneously:

1. Capture AI-assistant responses
2. Classify the underlying prompt by intent
3. Score the response's tone/stance toward your brand, ideally with dimensions beyond positive/negative

Most current products emphasize AI visibility, mentions, citations, sentiment and competitive share of voice. AirOps Slate

So if you're asking because you're considering building this, I think there's a genuine gap. The interesting product isn't just “AI brand monitoring”; it would be something closer to AI Response Intelligence:

Prompt → Intent → AI response → Brand stance → Competitive positioning → Evidence → Trend

For example, you could score each response on recommendation strength, sentiment, factual accuracy, differentiation, competitive preference, and confidence, while automatically tagging the prompt as review / recommendation / comparison / alternative / troubleshooting / purchase intent, etc.

That would be substantially more actionable than a generic “positive sentiment = 82%” dashboard.

Yes. What you’re describing is a combination of LLM-response evaluation + intent classification + brand/reputation analysis. There are tools that cover most of it, although the exact combination—“score how positively/appropriately an AI talks about our services, then segment by buyer intent”—is still a relatively specialized use case.

A few relevant options:

• persipica.com — probably closest if you mean how AI assistants represent/recommend your brand. It runs buyer questions across multiple AI assistants, tracks recommendation/visibility, compares competitors, and breaks performance down by question type such as research, comparison, and ready-to-buy. Persipica
• Braintrust / LangSmith / Langfuse / Promptfoo — better if you want to build your own evaluator. They let you define a rubric and have an LLM score responses for dimensions like tone, accuracy, helpfulness, brand alignment, while you separately classify each query as review/recommendation/comparison. Current evaluations commonly use exactly this LLM-as-a-judge pattern. Respan Inference.net
• Sentiment/aspect-analysis systems — useful for measuring whether the response is positive/neutral/negative about specific aspects of your service, rather than just overall sentiment. Modern sentiment analysis supports document-, sentence-, and aspect-level scoring. ScienceDirect

The metric I'd actually build

For your use case, I'd structure each AI answer as:

That gives you much more useful reporting than a generic “sentiment score.” For example:

Comparison intent: 72% positive tone, 4.1/5 accuracy, recommended in 61% of responses
Recommendation intent: 89% positive tone, 4.6/5 recommendation strength
Review intent: 54% positive, but recurring negative sentiment around pricing

There’s good precedent for scoring tone and task appropriateness separately with LLM-as-a-judge rather than collapsing everything into sentiment. doi.org

If what you’re evaluating is specifically ChatGPT/Claude/Gemini/Perplexity answers about your company, rather than your own customer-service chatbot, Persipica is the closest off-the-shelf match I found. If you tell me what your services are and which AI assistants you care about, I can also identify 3–5 tools that specifically do this and compare their capabilities/pricing.

Yes. What you’re describing is now a fairly distinct category: AI brand perception / AI search sentiment monitoring.

A few tools look particularly close:

• kime.ai — scores AI responses across dimensions including language tone, competitive position, endorsements, source credibility, risk, and confidence. This is probably the closest match if you want more than simple positive/negative sentiment. Kime
• prominenceai.io — has semantic sentiment scoring and lets you segment results by prompt segments, model, market, and category dimensions. It explicitly supports recommendation-oriented analysis. Prominence AI
• presenc.ai — tracks AI responses across major models and analyzes sentiment, brand position, accuracy, citations, and prompt types including recommendations and comparisons. Presenc AI
• tracemetry.com — focuses on how AI answers frame your brand: positive/neutral/negative, recommendations, competitor positioning, caveats, and stale/misleading descriptions. Tracemetry

For your exact use case

I’d structure the measurement as:

The important distinction is that plain sentiment isn't enough for AI responses. Most assistants avoid overtly negative language, so a response saying “X is a solid option, but Y is better for most businesses” may technically be positive while being commercially unfavorable. Prominence AI makes essentially this argument by benchmarking sentiment against competitors rather than treating the raw sentiment score as the primary signal. Prominence AI

So if your goal is specifically “How does AI talk about our services depending on what the buyer is trying to do?”, I'd prioritize KIME and Prominence AI, then evaluate Presenc AI if citation/source analysis is especially important.

If you're considering building this capability internally, I can also map out what the scoring/segmentation pipeline would look like and which parts are genuinely differentiated versus commodity LLM classification.

Yes. What you’re describing is usually built as an AI response evaluation + conversation intelligence layer rather than a single “tone score” product. The stack typically has three capabilities:

1. Tone / brand voice scoring

• Scores responses against a rubric such as: - helpful vs. dismissive
• confident vs. hedging
• professional vs. casual
• aligned vs. off-brand
• empathetic vs. robotic
• Some LLM evaluation platforms use “LLM-as-a-judge” scoring for dimensions like tone consistency and appropriateness. Noveum.ai
• Intent segmentation

• Classifies conversations by user intent, for example: - Review — “Is this service any good?”
• Recommendation — “Which service should I choose?”
• Comparison — “How does this compare to competitor X?”
• Pricing, support, complaints, onboarding, etc.
• Intent classification is a common NLU pattern used in conversational AI systems. arXiv
• Service / competitor perception analysis

• Tracks whether AI assistants describe your services positively or negatively.
• Measures: - share of mentions
• recommendation likelihood
• competitor inclusion
• feature associations
• recurring objections

Some tools in adjacent categories:

• tandemscore.com — evaluates AI agent responses across multiple quality dimensions and can score transcripts.
• noveum.ai — focuses specifically on evaluating tone consistency and alignment.
• enterpret.com — more customer-feedback oriented, combining sentiment and topic detection.
• Custom builds often use an LLM evaluator with a schema like:

Conversation:
- user intent: review | recommendation | comparison | other
- sentiment: positive | neutral | negative
- assistant tone score: 1-10
- brand alignment score: 1-10
- competitor mention: yes/no
- recommendation strength: weak/moderate/strong

For your use case (“how AI assistants talk about our services”), I’d look less at generic chatbot QA tools and more at an AI visibility / answer engine monitoring system: run hundreds of prompts across ChatGPT, Claude, Gemini, Perplexity, etc., capture responses, then score tone + intent + positioning.

If you mean monitoring how external AI assistants recommend your company to consumers, that’s a slightly different category from evaluating your own chatbot.

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.

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.