Answers that do not mention Peec AI

If you mean “track how ChatGPT/Claude/Gemini/Perplexity describe my brand, score the sentiment, and show whether it’s improving or declining over time,” there are several tools built specifically for that.

• scoutllm.ai — particularly aligned with your requirement. It labels mentions positive/neutral/negative, calculates a net sentiment score, and charts the distribution over time, with the underlying answer text. Scout LLM
• rankr.so — tracks visibility, position, and a 0–100 sentiment score across ChatGPT, Claude, Perplexity, and Gemini, with daily trends. Rankr
• gumshoe.ai — broader AI-brand intelligence; compares sentiment and recommendations across multiple models and provides time-series reporting. Gumshoe AI
• v3.nightwatch.io — monitors mentions and sentiment across five LLMs, with daily scans and surrounding context. Nightwatch
• hootsuite.com — worth considering if you already use Hootsuite; it combines LLM sentiment/visibility with traditional social and media monitoring. hootsuite.com

For your specific wording, Scout LLM or Rankr are the closest matches because sentiment trend over time is a core metric rather than just a one-off audit.

Gumshoe, Semrush AI Visibility, and Lettertrace all fit. They track how AI assistants describe your brand and show sentiment/visibility over time. Gumshoe AI Semrush Lettertrace

• Gumshoe — strongest dedicated AI-brand monitoring; tracks sentiment across ChatGPT, Gemini, Claude, Perplexity, etc., with time-series trends. Gumshoe AI
• Semrush AI Visibility — good if you already use Semrush; tracks sentiment, narratives, mentions, and competitor visibility. Semrush
• Lettertrace — open-source/developer-focused option with sentiment and week-over-week trends. Lettertrace

If your specific goal is “how positively does ChatGPT talk about my brand, and is that getting better or worse?”, I’d look first at Gumshoe or Lettertrace.

If your goal is specifically “what do ChatGPT/Gemini/etc. say about my local business, and how positive is it?”, I’d look at Pantora first. It is explicitly built for local businesses and tracks AI answers for local/customer questions, including whether you’re mentioned, your position, competitors, and how AI describes attributes such as quality, pricing, reliability, and responsiveness. It also provides ongoing trends and a monthly summary. Pantora

pantora.ai

Other good fits

• MentionScout — broader AI-answer monitoring across ChatGPT, Claude, Gemini, Perplexity, Grok, Google AI Overviews and AI Mode. It explicitly tracks mentions, sentiment, competitor share, citations and visibility scores, with scheduled prompts. MentionScout

mentionscout.com

• Rankability Tracker — useful if you want client/stakeholder reporting alongside traditional/local SEO. It measures AI mentions, citations, recommendations and sentiment and rolls them into a visibility metric. Rankability

rankability.com

• MentionPilot — particularly reporting-oriented: it combines AI mentions, sentiment and competitor share into a visibility score and offers automated digest reports. MentionPilot

mentionpilot.ai

For a single local business: Pantora.
For an agency managing multiple businesses: Rankability or MentionScout.
For a simple recurring executive report: MentionPilot.

One caveat: AI “sentiment” is an interpretation of the generated answer and/or the underlying web information, not a direct measurement of customer sentiment. For reporting, I'd retain the actual AI response, prompt, model, date, and sentiment score alongside the aggregate score so the report is auditable. Pantora specifically notes that a single AI answer shouldn't be treated as establishing a trend. Pantora

For a single local business: Pantora.
For an agency managing multiple businesses: Rankability or MentionScout.
For a simple recurring executive report: MentionPilot.

For your use case, I’d look at yext.com first.

It is specifically designed for local-business AI visibility reporting. Scout tracks how your business appears in AI-generated answers across ChatGPT, Gemini, Claude, and Perplexity, including:

• AI mentions / visibility — how often your business appears in relevant answers.
• AI brand sentiment — whether those mentions are positive, neutral, or negative. Yext Help Yext Help
• Local/competitor comparisons — performance by location and against nearby competitors. Yext
• Citation/source tracking — which websites AI systems are using when discussing your business. Yext Help
• Reporting over time — useful for showing clients whether AI visibility and sentiment are improving. Yext Help
• API/MCP access — Yext launched Scout API and MCP in 2026, which could be useful if you want to pipe the data into your own reporting dashboard. Yext

If you want something simpler

mentioned-on.com is worth testing for a smaller/local business. It runs a free scan across four AI assistants and offers ongoing monitoring from $99/month according to its current site. MentionedOn

mentionsflow.com is another AI-answer monitoring option that tracks mentions, citations, and comparisons across ChatGPT, Gemini, Claude, Perplexity, and Google AI Overviews. MentionsFlow

My practical pick: if you're producing client-facing local SEO/AEO reports, start with Yext Scout; if you're testing the concept for a single business and want a lightweight entry point, try MentionedOn first.

If your specific goal is “alert the marketing team when AI assistants start describing our brand more negatively,” you want an AI brand-monitoring / AI-search-visibility platform with sentiment alerts, rather than traditional social listening.

A few current options:

• Promptwatch — specifically tracks brand sentiment and positioning in AI-generated responses across assistants such as ChatGPT and Gemini. Promptwatch
• Otterly AI — monitors AI-search visibility and sentiment across multiple AI surfaces; useful if you want ongoing tracking rather than occasional audits. DataDab
• AthenaHQ — focuses on brand representation in LLM answers, including sentiment and contextual diagnostics. DataDab
• Profound — more enterprise-oriented, with broad AI-search monitoring and reporting. DataDab
• Citenzo — explicitly advertises alerts when AI visibility or sentiment changes, including changes in the sources influencing the AI's answers. Citenzo
• AskAiRank — tracks sentiment trends across multiple assistants and reports whether sentiment is moving up or down. AskAiRank

What I'd look for

The important distinction is whether the tool can alert on a change, rather than merely show a sentiment score. Ideally configure something like:

“Alert Slack/email when negative sentiment exceeds 10%, or when negative sentiment increases by ≥20% week-over-week, with the exact prompts and AI responses that caused the change.”

Also track which assistant changed, the specific prompts producing the negative descriptions, competitors being recommended instead, and the web sources cited by the assistants. These tools generally work by repeatedly running a defined prompt set and analyzing the resulting AI answers. arbling.com

If you tell me your company size/budget and whether you care about ChatGPT, Claude, Gemini, Perplexity, or all of them, I can narrow this to 2–3 tools and compare their alerting/integration capabilities.

A few tools specifically do this:

• Semrush AI Visibility — tracks sentiment, narratives, and topics across ChatGPT, Gemini, Perplexity, and Google AI, with daily prompt tracking. Semrush
• Nightwatch AI Brand Monitoring — monitors multiple LLMs and provides brand-mention and sentiment alerts. Nightwatch
• Lolly — tracks AI-generated brand mentions, sentiment, accuracy, and share of AI voice. Lolly
• BrandReply — runs prompts daily and alerts when an AI answer changes, including sentiment changes. BrandReply

For a marketing team wanting an actual alert when sentiment deteriorates, I'd shortlist Semrush and Nightwatch first based on their documented monitoring/alert capabilities. Semrush Nightwatch

Yes. There’s now a fairly mature category of AI visibility / GEO monitoring tools that track how AI assistants describe brands, including sentiment, mentions, share of voice, regions, and languages.

A few relevant options:

• semrush.com — Tracks brand sentiment, narratives, and share of voice across major AI platforms, with enterprise coverage across many location-language combinations. Semrush Semrush
• getmentions.ai — Specifically offers sentiment trends, visibility, average rank, markets, and languages, with historical trend tracking and chart exports. GetMentions
• menra.ai — Runs prompts across multiple AI engines and provides sentiment plus regional drill-downs; it explicitly supports region- and language-aware tracking. Menra
• yozigo.ai — Tracks visibility, position, sentiment, citations, and share of voice across AI engines and regions, with daily measurements. Yozigo
• psentry.com — Particularly focused on multilingual/international brands, with cross-language and cross-market visibility trends. PSentry
• pi-datametrics.com — Combines AI brand mentions, sentiment, queries, competitors, and the sources/citations influencing the narrative. Pi Datametrics

If your exact need is a visualization

The data model you probably want looks something like:

For an enterprise use case, I'd look for raw-answer evidence behind every sentiment point, not just a proprietary score. SignalKit, for example, says it retains the underlying AI answer and shows the context behind its sentiment measurement. signalkit.ai

If you tell me which AI engines, regions, and languages you need, I can narrow these down to the tools that actually match your coverage requirements and compare their pricing/features.

Yes. A newer category of tools does this: AI answer visibility / generative engine optimization (GEO) analytics platforms. They monitor how AI assistants describe your brand, then visualize metrics like sentiment, mentions, share of voice, competitors, regions, and languages. Aureol GetMentions

Some options to look at:

Typical dashboard views include:

• Sentiment over time

Example: “Positive AI mentions rose from 62% → 71% after a product launch.”

• Heatmaps by market

• US: positive
• Germany: neutral
• Japan: mixed

(based on tracked prompts and AI responses)

• Language comparisons

• English vs. French vs. Spanish answers
• Whether localization changes how AI describes the brand
• Model-by-model views

• ChatGPT
• Gemini
• Claude
• Perplexity
• Google AI Overviews / AI Mode (depending on vendor) GetMentions Lumar

When evaluating one, I’d check whether it can:

1. Run the same prompts across countries/languages (not just translate dashboards).
2. Store the actual AI answers behind each sentiment score.
3. Separate brand sentiment from general web sentiment.
4. Show which sources/citations are causing perception changes.
5. Export data to BI tools if you need executive reporting.

If you’re looking for an enterprise-grade brand monitoring setup (hundreds of markets/languages), the shortlist would likely differ from a startup/SEO team use case.

Yes. What you’re looking for is generally called AI search / GEO (generative engine optimization) brand monitoring rather than traditional web crawling. These platforms submit representative prompts to AI assistants, capture the resulting answers, and extract things such as brand mentions, citations, position, and sentiment. TechnologyAdvice Tracemetry

A few options worth evaluating:

• gumshoe.ai — Tracks how brands are represented across ChatGPT, Gemini, Claude, Perplexity and other models, including sentiment, competitors, personas, and longitudinal trends. Gumshoe AI
• birdeye.com — Particularly interesting if you need structured sentiment reporting; it reports positive/neutral/negative AI-generated mentions and supports ChatGPT, Gemini, Perplexity, Claude, Grok and Google's AI experiences. Birdeye Help Center
• modelmention.io — Focuses on AI visibility, mentions, sentiment, citations and sources across ChatGPT, Gemini and Perplexity. ModelMention
• visimatrix.com — Tracks seven assistants, including ChatGPT, Gemini, Claude, Perplexity, DeepSeek, Mistral and Grok, with mention rate, rank, sentiment and citations. VisiMatrix
• tutka.ai — Combines AI-answer monitoring with broader web mention monitoring (Reddit, Quora, news, blogs), including sentiment and source attribution. Tutka
• mentionsapi.com — More developer-oriented. Its API is specifically designed to run scheduled prompts against ChatGPT, Claude, Gemini and Perplexity and return mentions, position, sentiment and citations. MentionsAPI

If you specifically need "sentiment-labeled snippets"

I'd distinguish two requirements:

1. Capture the actual surrounding text — e.g. “Acme has excellent integrations but its enterprise pricing is high.”
2. Label that occurrence — e.g. positive, negative, or neutral, ideally with the relevant topic/aspect.

The tools above generally provide the latter, but check the export/API before buying if you need the former as a machine-readable field. Some products emphasize dashboards and aggregate sentiment rather than giving you every raw snippet.

If you're building this into a brand-intelligence pipeline, MentionsAPI or another API-first product is probably the most relevant category; if you're looking for a ready-made marketing dashboard, Gumshoe/Birdeye/VisiMatrix are closer to that use case. MentionsAPI Birdeye Help Center VisiMatrix

Also, these systems generally sample prompts and query the assistants, rather than literally crawling the private conversations of users. For example, GeoEcho explicitly describes using rotating samples of realistic prompts and treating the resulting figures as a statistical sample. GeoEcho

Yes. What you’re looking for is generally called AI visibility / GEO monitoring rather than traditional web crawling. These tools repeatedly query AI assistants, save the answers, detect your brand mentions, and often extract the surrounding text plus sentiment.

A few options that specifically fit your requirement:

• searchprism.io — Particularly close to your description. It stores the complete AI answer, counts brand mentions, extracts the context/sentiment, and tracks cited URLs. It monitors ChatGPT, Claude, Gemini, and Perplexity. Prism
• visimatrix.com — Tracks ChatGPT, Gemini, Claude, Perplexity, DeepSeek, Mistral, and Grok, with mention rate, sentiment, citations, and competitor monitoring. VisiMatrix
• gumshoe.ai — Focuses heavily on how AI talks about a company, including sentiment, buyer personas, competitors, and multiple AI models. Gumshoe AI
• linksii.com — Tracks brand mentions across ChatGPT, Claude, Gemini, and Perplexity and classifies individual mentions as positive/neutral/negative. Linksii
• mentionsapi.com — More interesting if you want to build your own pipeline. Its API extracts mentions, positions, sentiment, and cited sources from ChatGPT, Claude, Gemini, and Perplexity. MentionsAPI
• oneglanse.com — Open-source option that can run on your own infrastructure and tracks mentions/sentiment across several LLM providers. OneGlanse

If your exact requirement is “give me the snippets”

I'd distinguish between two things:

1. Visibility monitoring: “Did ChatGPT mention Acme, and how frequently?”
2. Evidence extraction: “Show me the exact passage where ChatGPT mentioned Acme, classify that passage as positive/neutral/negative, and retain the underlying answer.”

For #2, SearchPrism, MentionsAPI, and AI Rank Lab look particularly aligned. AI Rank Lab explicitly says it highlights the brand in the exact AI response/snippet and assigns positive/neutral/negative sentiment. AI Rank Lab

If you're building this for a PR/brand-intelligence workflow, I'd also look at whether the product can export the raw answer rather than just a sentiment score. That matters because an LLM's sentiment classifier can miss distinctions such as “widely used, but expensive and unreliable”.

If you tell me whether you want (a) a SaaS tool, (b) an API, or (c) something self-hosted, I can narrow this to the most suitable 3–5 options and compare pricing, assistant coverage, raw-snippet access, sentiment, and API/export capabilities.

I’d use an LLM observability + evaluation layer, with Evidently as a strong open-source option. It supports continuous monitoring and specifically includes sentiment, toxicity, tone, and custom evaluations. Evidently AI

For a PR crisis/product launch, set up:

• Baseline: capture 2–4 weeks of pre-event answers and sentiment.
• Segment: track sentiment by topic, customer type, prompt, model/version, and channel.
• Metrics: positive/neutral/negative sentiment, frustration, toxicity, refusal rate, answer quality, and key crisis-related topics.
• Change detection: compare rolling post-event distributions against the baseline and alert on statistically meaningful shifts.
• Human review: sample flagged answers to distinguish genuine sentiment changes from classifier/model artifacts.
• Timeline: annotate launches, PR incidents, prompt changes, and model upgrades so shifts can be correlated with events.
• Dashboard: show sentiment trajectory alongside answer-quality and user-feedback metrics; sentiment alone can be misleading. AWS also recommends monitoring sentiment and toxicity shifts in LLM outputs. Amazon Web Services, Inc.

If you want a more turnkey production setup, LangSmith or Confident AI are alternatives with trace-level evaluations and production monitoring. LangChain confident-ai.com

If your goal is “did AI answers about our brand get more negative after the event?”, I’d build a lightweight AI-answer sentiment drift monitor rather than rely on conventional social-listening sentiment.

Recommended solution

Use a fixed prompt panel + scheduled answer collection + aspect-level LLM grading + before/after analysis.

1. Establish a baseline

Before the launch/crisis, create 50–200 representative prompts such as:

• “What are the best products for X?”
• “Is [brand] reliable?”
• “What are the drawbacks of [brand]?”
• “How does [brand] compare with competitors?”
• “Should a company buy [product]?”
• Crisis-specific questions: “What happened with [brand]?” / “Is [brand] trustworthy after [incident]?”

Run each prompt repeatedly across the AI systems/models you care about. Repetition matters because AI responses can vary.

2. Score the answer, not just the mention

I'd use a structured grader with dimensions like:

This is preferable to simple positive/negative keyword counting because AI answers often express sentiment through hedging, confidence, comparisons and framing rather than explicit emotional language. Asky

LLM-based graders are practical for this: current evaluation tooling supports both label-based and score-based grading, including custom criteria. OpenAI Platform OpenAI Developers

3. Track the delta around the event

Define:

• T−30 to T−1: baseline
• T0: launch/crisis
• T+1 to T+7: immediate reaction
• T+8 to T+30: stabilization/recovery

For every prompt/model pair, calculate:

Sentiment shift = post-event score − baseline score

But don't collapse everything into one number. I'd show the distribution by:

• model
• prompt category
• geography/language
• product
• sentiment dimension
• narrative/theme

A useful dashboard might look like:

AI Brand Perception

Overall sentiment: +0.18 → −0.06

Trust: 4.1 → 3.2

Crisis association: 0.8 → 3.7

Negative answers: 12% → 31%

Biggest shift: “customer support” prompts

Most frequently emerging narrative: “uncertainty about reliability”

4. Preserve the actual answers

This is important for PR/crisis work. Don't just store scores.

Store:

timestamp → model → prompt → answer → cited sources → scores → extracted narratives

Then your dashboard can answer “why did sentiment move?”, rather than merely reporting that it moved.

Current AI-evaluation practice also emphasizes continuously evaluating real outputs after launch and keeping humans involved in auditing automated graders. OpenAI OpenAI

5. Add anomaly alerts

I'd trigger an alert when, for example:

• sentiment falls >10% from the rolling baseline
• negative-answer share doubles
• a new negative narrative appears in ≥10% of answers
• a competitor starts being mentioned substantially more often
• a particular model changes disproportionately
• crisis-related language persists beyond a defined recovery window

The alert should include the 5–10 answers responsible for the shift, not just “sentiment decreased.”

What I'd use

For a team building this internally, my preferred architecture would be:

Prompt registry → model/API runner → response warehouse → LLM graders → statistical drift detection → dashboard/Slack alert

You don't necessarily need a specialized “AI sentiment” SaaS product. Existing evaluation infrastructure can provide much of the grading layer; OpenAI's evaluation tooling, for example, supports reusable evaluation criteria, multiple grader types, and repeated runs. OpenAI Evals OpenAI Platform

If you want the fastest route to production, though, there are now products explicitly offering before/after AI-answer sentiment tracking and model/prompt-level drilldowns. ChatBakers Profound Help Center

My key recommendation: treat this as “AI perception monitoring” rather than ordinary sentiment analysis. The most valuable signal after a crisis isn't simply positive vs. negative; it's which narratives AI systems are adopting, how confidently they're expressing them, and whether those narratives persist across models and over time.

Yes. What you’re describing is now a fairly specific category: LLM/AI visibility monitoring + citation/source intelligence + sentiment analysis.

The closest matches I found are:

• llmpulse.ai — probably the closest to your exact idea. It captures the full AI answer, identifies every citation/source, and scores the answer sentiment. Its citation-source analysis can flag sources associated with potentially negative or confirmed-negative answers. LLM Pulse LLM Pulse
• visibility.so — shows the exact AI answers, sentiment, and the sources cited for each prompt. It also has source rankings showing which URLs AI engines cite most frequently. Visibility.so
• wellows.com — explicitly connects citation → source URL → AI answer → sentiment. It says it classifies each citation by sentiment and lets you see the exact URLs driving AI visibility. Wellows
• mybrandi.ai — particularly focused on the why behind AI sentiment, attempting to connect source material such as media coverage and content to how LLMs describe a brand. Brandi AI
• sona.com — tracks sentiment alongside citation authority, including domain/URL-level source attribution and source-type breakdowns. Sona
• llmvisibility.tech — combines sentiment with the sources behind answers and lets you drill down by prompt/model/date. MentionScore

The distinction I'd pay attention to

There are actually two different products hiding inside your question:

1. "What sources does AI cite?"

Reddit → 32 citations
Forbes → 18
G2 → 14
Your website → 7

That's citation/source tracking.

2. "What happens to the answer when those sources are cited?"

For example:

The second one is much more interesting and much less commoditized.

LLM Pulse, Wellows, Brandi AI and Sona appear particularly close to this because they aren't merely counting citations—they're attempting to connect the source/evidence layer to the sentiment of the generated answer. Wellows LLM Pulse Brandi AI Sona

If you're asking because you're thinking about building a product around this, there's an interesting gap: "Which sources cause AI to say positive vs. negative things about my brand?" is more actionable than conventional AI-visibility scores, and I can map out what these platforms currently do vs. what appears to be missing.

Yes. This is now a distinct category of AI visibility / LLM citation monitoring, and a few platforms are getting quite close to exactly what you described.

The important distinction is between:

1. “Which sources does AI cite?”
2. “What sentiment does the AI answer have?”
3. “Which specific sources correlate with positive vs. negative answers?”

The third is less commonly exposed as a clean source-level metric, but some tools are starting to provide it.

Platforms worth looking at

• LLM Pulse — probably one of the closest matches. It tracks the full AI response, citations, and sentiment, and lets you identify sources associated with negative responses. Its documentation specifically describes Potentially Negative citations based on the sentiment of the citing AI response. llmpulse.ai

• Wellows — explicitly combines citation tracking with sentiment. It captures the full AI answer, classifies it as positive/neutral/negative, and connects the answer back to the exact third-party URLs cited. wellows.comllmpulse.ai

• Visibility.so — tracks individual AI answers with sentiment and their cited sources, plus source rankings. This could give you the raw data needed to determine which sources appear in positive vs. negative answers. visibility.so

• Sona — separates sentiment analysis from citation authority, with domain/URL-level source attribution and per-prompt source attribution. www.sona.comllmpulse.ai

• SourceWatch — tracks citations, sentiment, and the sources AI trusts across multiple engines. sourcewatch.ai

The feature you're describing is slightly more specific

What I would call the ideal dashboard is something like:

Then you could drill down:

Source → cited URL → prompt → AI model → exact answer → sentiment

That's more useful than simply saying “Reddit is cited 420 times.” It tells you whether Reddit citations tend to accompany favorable, neutral, or unfavorable representations.

There is also research supporting why this distinction matters: a 2026 study of 167,551 URL-grounded citations found that 85.7% of citations to brands came from third-party sources, rather than the brands' own sites. arxiv.org

If you're asking because you're thinking about building this: I think there is still a meaningful product gap between citation tracking and source → sentiment attribution. Most existing tools expose both datasets, but don't appear to make the “this source is responsible for X% of positive/negative AI answers” relationship the primary analytical object. llmpulse.ai

If you want, I can also map out the existing competitors and exactly what each one does/not does, including which have APIs and which could be closest to building this product.

If by “AI answer sentiment data” you mean tracking how ChatGPT/Perplexity/Gemini/etc. describe your brand—mentions, sentiment, position, citations—and then connecting that to website traffic, conversions, and revenue, I’d shortlist these:

My practical recommendation

For the specific use case you described, I'd look first at Cairrot or Analyze AI.

Cairrot is particularly aligned with the problem because it explicitly combines AI-answer visibility, citations, sentiment and LLM traffic with GA4, Search Console, Bing Webmaster Tools and BI tools, allowing you to analyze AI visibility alongside conversions. Cairrot

cairrot.com

Analyze AI similarly connects AI-answer appearance/placement/sentiment with GA4 sessions, conversions and revenue. Analyze AI

tryanalyze.ai

If you're already heavily invested in HubSpot, its AEO product is compelling because it combines AI visibility, sentiment, citations and CRM data; HubSpot says its AEO product starts at $50/month. HubSpot

hubspot.com

The measurement setup I'd aim for

The important thing isn't simply:

AI sentiment ↑ → conversions ↑

I'd build a dataset like:

AI prompt → AI engine → brand mentioned? → position → sentiment → cited source → AI referral → landing page → session → conversion → revenue

Then you can answer questions such as:

• Does positive AI sentiment correlate with higher conversion rates?
• Which AI engines send the highest-value visitors?
• Which prompts produce visitors who actually buy?
• Does moving from position #5 to #1 in an AI answer affect traffic?
• Which third-party sites/citations are associated with positive AI recommendations?
• Are AI-referred visitors converting differently from Google organic visitors?

For the underlying web/conversion data, GA4 → BigQuery is particularly useful because Google exposes user-, session-, and event-level traffic attribution, including attribution fields on conversion events, allowing you to join GA4 data with other datasets. Google for Developers

If you tell me your stack (GA4/HubSpot/Salesforce, Shopify or SaaS, monthly traffic, and whether you care about ChatGPT/Perplexity/Google AI specifically), I can narrow this to 2–3 options and compare pricing, integrations, attribution depth, and how you'd actually measure incremental conversions.

cairrot.com

Analyze AI similarly connects AI-answer appearance/placement/sentiment with GA4 sessions, conversions and revenue. Analyze AI

tryanalyze.ai

If you're already heavily invested in HubSpot, its AEO product is compelling because it combines AI visibility, sentiment, citations and CRM data; HubSpot says its AEO product starts at $50/month. hubspot.com

hubspot.com

The measurement setup I'd aim for

The important thing isn't simply:

AI sentiment ↑ → conversions ↑

I'd build a dataset like:

AI prompt → AI engine → brand mentioned? → position → sentiment → cited source → AI referral → landing page → session → conversion → revenue

Then you can answer questions such as:

If by “AI answer sentiment data” you mean how ChatGPT/Claude/Gemini/Perplexity describe your brand—positive/negative/neutral, inclusion, competitors mentioned, prompts, etc. and you want to connect that to actual website traffic and conversions, I’d look at a two-tool stack rather than one platform.

My shortlist

• similarweb.com + Amplitude — probably the closest fit to what you're describing. Similarweb's Gen-AI Intelligence tracks AI brand visibility, sentiment, prompts, AI engines, and AI-driven website traffic; Amplitude can then connect acquisition sources and user behavior to conversion funnels and revenue. Similarweb Similarweb Knowledge Center Amplitude
• amplitude.com — strongest choice for the conversion/behavior side. It supports channel attribution, funnels, conversion rates, campaign analysis, and multi-touch attribution. Amplitude Amplitude
• similarweb.com — strongest choice for the AI visibility side. It specifically reports AI traffic, top prompts, AI engines, pages receiving AI traffic, and AI brand sentiment/visibility. Similarweb

The setup I'd use

Think of the data model as:

AI sentiment / visibility → AI traffic → website behavior → conversion → revenue

For example:

The important distinction is that AI sentiment alone doesn't demonstrate conversion impact. You want to join it with referral/session and conversion data, ideally by date, AI platform, landing page, topic/prompt, and campaign/content change.

Similarweb is particularly interesting here because it already combines AI visibility/sentiment with AI referral traffic. Similarweb Amplitude then gives you much deeper funnel and attribution analysis downstream. Amplitude Amplitude

If I were building this for a marketing team, I'd start with Similarweb + Amplitude, with a warehouse/BigQuery layer if you need rigorous longitudinal analysis.

If you tell me B2B vs B2C, approximate monthly traffic, and whether you're measuring leads or ecommerce purchases, I can narrow this down to 2–3 specific platforms and explain the likely cost/setup tradeoffs.

Yes. This is now a category of AI/LLM brand-monitoring or AI-search-visibility tools.

• Lolly — scores sentiment/accuracy of AI answers and tracks prompts such as recommendations and vendor comparisons. Lolly
• Scout LLM — scores positive/neutral/negative framing and lets you inspect the underlying answer. Scout LLM
• Semrush AI Visibility — tracks sentiment, narratives, topics, and competitive visibility across AI answers. Semrush
• LLM Visibility — combines sentiment/position scoring with buying-intent prompts. LLM Visibility

For your exact use case, I'd look for a tool that lets you define an intent taxonomy—e.g. review, recommendation, comparison, alternative, pricing, how-to—and then score tone + factual accuracy + positioning separately for each intent. Many current tools do the first pieces, but intent-level segmentation is less consistently exposed as a first-class feature. Lolly infin8content.com

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.

If your goal is specifically “keep historical AI responses, score their sentiment, and demonstrate that sentiment improved over time,” I’d look at langfuse.com first.

Why it fits:

• Historical traces: it stores production AI interactions and their outputs, so you can retain the underlying evidence rather than just an aggregate metric. Langfuse Langfuse
• Custom sentiment scoring: you can create an LLM-as-a-judge evaluator for sentiment, tone, helpfulness, etc., and attach the resulting score to each response. Langfuse
• Time-series evidence: its Score Analytics can show scores trending over time, which is directly useful for demonstrating change between releases or periods. Langfuse
• Historical re-evaluation: you can apply a new evaluator to historical observations, letting you consistently score old responses with the same rubric. Langfuse
• Experiments: you can run the same historical dataset through different prompt/model versions and compare the resulting scores. Langfuse
• Auditability: user feedback, human annotations, automated judges, and corrections can all be stored as scores associated with the underlying trace. Langfuse

Another option: Braintrust

braintrust.dev is also worth considering if your primary requirement is experiment/regression tracking. Its platform explicitly presents experiment results as historical data points and supports automated scorers, LLM judges, human review, datasets, and trend tracking. Braintrust

How I'd structure the evidence

Rather than measuring only a generic “sentiment” number, I'd preserve the raw response plus several fixed dimensions:

Then you can produce a defensible chart such as “median sentiment by month, using the same evaluator rubric and comparable traffic segments”, while retaining the individual responses behind every data point.

For your stated use case, I'd choose Langfuse if historical traceability and longitudinal measurement are the priority; Braintrust is another strong candidate if experiment/regression analysis is the center of the workflow.

If your goal is specifically to prove that AI answer sentiment/quality improves over time, I’d look first at braintrust.dev.

It is built around the workflow you’re describing: versioned datasets → scored AI responses → experiments → historical comparisons. You can preserve the exact prompt/model/version and evaluator scores, then compare releases over time rather than relying on anecdotal examples. Arize AI MarkTechPost

What I’d track

For every AI answer, store something like:

Then your reporting can show, for example:

Answer sentiment
0.71 → 0.76 → 0.81 → 0.84

Helpful-answer rate
78% → 83% → 89% → 92%

The important part is versioning the evaluator itself. Otherwise, changing the sentiment judge can make historical improvements look better or worse even when the underlying answers didn't change.

Alternatives worth considering

• langfuse.com — particularly attractive if you want open-source/self-hosted infrastructure and durable traces/evaluations.
• smith.langchain.com — strong choice if you're already using LangChain/LangGraph; it combines traces, datasets and evaluations.
• arize.com / Phoenix — worth considering if you need broader production monitoring and drift analysis. Arize AI Arize AI

For your exact requirement, I'd structure it as an "AI quality ledger," not merely a sentiment dashboard. Sentiment alone can be misleading: an answer can become more positive while becoming less accurate. Store sentiment alongside correctness, helpfulness, safety, and human feedback so you can demonstrate what improved and why.

For every AI answer, store something like:

Then your reporting can show, for example:

Answer sentiment
0.71 → 0.76 → 0.81 → 0.84

Helpful-answer rate
78% → 83% → 89% → 92%

The important part is versioning the evaluator itself. Otherwise, changing the sentiment judge can make historical improvements look better or worse even when the underlying answers didn't change.

Alternatives worth considering

• langfuse.com — particularly attractive if you want open-source/self-hosted infrastructure and durable traces/evaluations.
• smith.langchain.com — strong choice if you're already using LangChain/LangGraph; it combines traces, datasets and evaluations.
• arize.com / Phoenix — worth considering if you need broader production monitoring and drift analysis. Arize AI Arize AI

For your exact requirement, I'd structure it as an "AI quality ledger," not merely a sentiment dashboard. Sentiment alone can be misleading: an answer can become more positive while becoming less accurate. Store sentiment alongside correctness, helpfulness, safety, and human feedback so you can demonstrate what improved and why.

If by “AI assistant mentions” you mean mentions of your brand in AI-generated answers, Semrush’s AI PR Toolkit / Media Monitoring is a strong match. It supports:

• Filtering mentions and viewing sentiment.
• An AI-Cited Mentions metric for coverage referenced in AI-generated answers.
• Selecting mentions and exporting them as CSV or Excel.
• Exporting broader monitoring reports as PDF or shareable dashboards. Semrush Semrush

semrush.com

If you specifically mean saving/exporting media “clips”, Mention also supports sentiment filtering and exporting filtered mentions in PDF, Excel, CSV, or TSV. Mention Help Center Mention

For a PR team, the key distinction is: Semrush = particularly relevant if AI citations/AI visibility are what you want to monitor; Mention = broader conventional web/social mention monitoring and exports.

If you mean mentions of your brand inside AI assistants like ChatGPT, Claude, Gemini, or Perplexity, Qwairy looks like the closest fit. It monitors AI responses across 10+ AI engines, scores sentiment, tracks narrative shifts, and provides exportable sentiment reports. Qwairy

If by “clips” you mean traditional news/social media clips, EyeOut is a closer match: it filters mentions by sentiment and lets PR teams save/tag coverage and export clips and trends. EyeOut

For a PR team specifically wanting AI-assistant monitoring + sentiment + media-monitoring workflow, I'd look at Qwairy first, while EyeOut is more oriented toward conventional media monitoring. Qwairy EyeOut

If by “multiple AI assistant sources” you mean monitoring what ChatGPT, Claude, Gemini, Perplexity, Grok, etc. say about your company/product every day, I’d treat this as AI reputation / AI visibility monitoring, not traditional LLM observability.

There are two good enterprise approaches:

1. Buy an AI-visibility monitoring platform

This is the fastest route. Current platforms can run the same buyer/customer questions across multiple assistants, capture the answers, measure sentiment/visibility, identify recurring themes, preserve citations, and send scheduled reports.

For example:

• Saidly monitors ChatGPT, Claude, Gemini and Grok, with per-model sentiment, quotes, themes, cited sources, and scheduled daily reporting; it also offers enterprise plans. Saidly Saidly
• Foglift monitors five AI engines and offers daily monitoring, alerts, API access, competitor sentiment and enterprise reporting. Foglift Foglift
• MentionsFlow provides multi-engine monitoring, share of voice, citations, scheduled PDF/CSV reports, alerts and API/MCP access. Mentions Flow
• Etyma runs prompts across multiple answer engines daily and reports brand presence, sentiment, competitors and cited sources. Etyma

For an enterprise, I'd require raw answers + source citations + prompt-level history, rather than accepting a single opaque “sentiment score.”

2. Build an internal “AI perception intelligence” layer

For a large enterprise, this is probably the more scalable architecture:

                         ┌─ ChatGPT
                         ├─ Claude
Prompt Library ──────────┼─ Gemini
                         ├─ Perplexity
                         └─ Grok
                               │
                               ▼
                    ┌─────────────────────┐
                    │ Response Collection │
                    └──────────┬──────────┘
                               │
                 ┌─────────────┼─────────────┐
                 ▼             ▼             ▼
             Sentiment      Themes       Citations
             /tone          /issues      /sources
                 │             │             │
                 └─────────────┼─────────────┘
                               ▼
                    Historical Data Warehouse
                               │
                 ┌─────────────┼─────────────┐
                 ▼             ▼             ▼
             Daily email     Dashboard      Alerts
             executive       BI/Looker      Slack
             briefing

The key is to standardize the prompts. For example, instead of randomly asking each model about your company, maintain a library such as:

• “What are the best vendors for [category]?”
• “What are the weaknesses of [company]?”
• “Would you recommend [company] to an enterprise buyer?”
• “How does [company] compare with [competitor]?”
• “What do customers complain about?”
• “Is [product] reliable?”
• “What are the alternatives to [product]?”
• “What should a CIO know before buying [product]?”

Run those questions every day and retain the complete response, not just the sentiment classification.

What I would put in the daily enterprise report

Executive summary

• Overall change since yesterday/7 days
• Biggest positive and negative changes
• New issues appearing in AI responses
• Competitors gaining/losing visibility

By AI assistant

I'd be careful with the numerical sentiment itself. The underlying responses are the evidence; the score is an analytical abstraction. Foglift, for example, explicitly preserves the answer text and treats citations as evidence rather than proof of why an AI generated particular wording. Foglift

The important enterprise distinction

Don't confuse this with LLM observability.

Tools such as LangSmith, Langfuse, Arize, Braintrust, etc. primarily monitor your own AI applications—traces, prompts, responses, evaluations, latency, cost, quality, etc. Confident AI LangChain

What you're describing is closer to:

“What are the world's AI assistants telling our customers about us?”

That is a different monitoring layer.

My recommended enterprise setup

I'd use a hybrid:

AI-monitoring vendor → warehouse → enterprise BI/reporting

Rather than having executives depend on a vendor's dashboard, ingest the underlying daily observations into your own data platform.

Your canonical record could be:

date
organization
prompt_id
prompt
assistant
model/version
response
sentiment
sentiment_confidence
themes[]
competitors_mentioned[]
citations[]
visibility
recommendation
raw_response_hash

Then generate the daily executive report from your own dataset.

This gives you historical benchmarking, model-to-model comparisons, competitor tracking, trend detection and the ability to change vendors later without losing your history.

If you're evaluating this as a product/business opportunity rather than just looking for a vendor, I can also lay out the ideal enterprise product architecture, data model, APIs, prompt strategy, scoring methodology, and daily-report format for building this yourself.

If by “daily sentiment reports” you mean what multiple AI assistants say about an enterprise’s brand/products/competitors, I’d treat this as an AI visibility + reputation monitoring system rather than simply sentiment analysis.

There are two sensible approaches:

1. Buy an enterprise monitoring platform — fastest

Several current platforms already run standardized prompts across multiple AI engines and return mentions, sentiment, citations, competitors, and historical trends.

• foglift.io — monitors ChatGPT, Perplexity, Claude, Gemini and Google AI Overview daily, with sentiment/mention tracking and enterprise features such as RBAC and scheduled reports. Foglift
• mentionsflow.com — covers ChatGPT, Claude, Gemini, Perplexity and Google AI Overview, with daily tracking and scheduled reports. Mentions Flow
• sellm.io — more API-oriented; supports ChatGPT, Claude, Perplexity, Gemini, Grok and Copilot, including asynchronous bulk analysis and webhooks. This is particularly interesting if you want to integrate results into an existing enterprise data platform. Sellm
• mentionsapi.com — provides a normalized API across ChatGPT, Claude, Gemini and Perplexity, including brand position, sentiment, surrounding context and cited URLs. MentionsAPI

2. Build an internal enterprise pipeline — best for control

For a large enterprise, I'd architect it like this:

                    ┌─ ChatGPT
                    ├─ Claude
Prompt Library ─────┼─ Gemini
                    ├─ Perplexity
                    ├─ Copilot
                    └─ Other AI surfaces
                           │
                           ▼
                 Response Collection Layer
                           │
                 ┌─────────┴─────────┐
                 │                   │
          Raw Responses        Citation URLs
                 │                   │
                 ▼                   ▼
          Sentiment/Entity      Source Analysis
             Extraction
                 │                   │
                 └─────────┬─────────┘
                           ▼
                    Data Warehouse
                           │
                           ▼
                  Daily Report Engine
                           │
             ┌─────────────┼─────────────┐
             ▼             ▼             ▼
           Email         Slack        Dashboard

The important part is to store the complete AI response, not just a sentiment score. This lets you re-analyze historical responses when your methodology changes. Current monitoring products similarly emphasize retaining answer text, mentions, competitors and cited sources. Prism MentionsAPI

What I'd put in the daily report

Don't make the report simply:

Sentiment: 73% positive

That's too lossy for enterprise use.

Instead:

Then include:

Top sentiment changes

• Product A: positive → neutral
• Product B: neutral → negative
• Competitor X: increased recommendations

Most important AI responses

• 5–10 representative responses
• Which model produced them
• Prompt
• Brand position
• Sentiment
• Citations/sources

Why sentiment changed

• New review/article being cited
• Product announcement
• Pricing information
• Competitor content
• Model/provider change

Recommended investigation

• Links to the underlying AI response and source documents
• No automatic conclusion that a particular marketing action caused the change

The key enterprise design decision

I'd separate measurement from interpretation.

For every response, preserve:

organization
brand
country / locale
prompt
timestamp
AI provider
model
raw response
brand mentioned?
brand position
competitors mentioned
sentiment
sentiment confidence
citations
cited domains
product/entity discussed

Then calculate higher-level metrics such as share of voice, mention rate and sentiment from those underlying observations.

This matters because AI responses are nondeterministic; a single response isn't a reliable measurement. Repeated standardized prompts over time give you a much more meaningful signal. Mentions Flow Tracemetry

My recommendation for an enterprise

If you're buying rather than building, I'd shortlist Foglift, Sellm, and MentionsAPI and evaluate them against your security, API, provider coverage, data-retention and reporting requirements.

If you're building this as an internal platform or SaaS product, I'd lean toward an API-first architecture such as Sellm/MentionsAPI rather than building six independent provider integrations yourself. Both explicitly support normalized multi-provider monitoring, while Sellm also provides bulk asynchronous analysis and webhooks. Sellm MentionsAPI

The resulting product could be much more useful than a generic “daily sentiment report”: “What are AI assistants telling our customers about us today, how did that change from yesterday, which sources are driving it, and which competitors are gaining visibility?”

If you tell me roughly how many brands × prompts × countries × AI assistants you need to monitor, I can lay out a concrete enterprise architecture and estimated monthly cost.

If you mean “we changed a page on our website, and then want to know whether/how that change altered the sentiment and wording of AI answers about us,” the closest-fit tools I found are:

• tryprofound.com — probably the strongest match. Its Sentiment feature tracks sentiment over time, extracts the exact AI wording behind the sentiment, and attributes sentiment signals to specific cited URLs/pages. That lets you investigate whether an updated page is driving a change in how AI describes you. Profound Help Center
• lumear.ai — particularly relevant if you want explicit content-change → AI-impact attribution. It says it matches AI responses against fresh crawls of your site and can tie changes in visibility/sentiment/citations to content changes you shipped. Lumear AI
• foglift.io — good for maintaining evidence around sentiment shifts. It tracks dated sentiment, exact answer text, cited sources, recurring negative themes, and later checks whether those themes improve after a page/source change. Foglift
• wellows.com — useful for broader prompt-level monitoring across ChatGPT, Gemini, Perplexity and Google AI surfaces, including sentiment, citations and historical changes. Wellows

What I'd look for technically

The important distinction is between correlation and true attribution. A solid setup should record:

content version → publish timestamp → prompt → AI model → exact answer → sentiment/themes → cited URL → subsequent answer

Then you can ask something like:

“We changed /pricing on September 3. Did the sentiment of answers to our 25 pricing-related prompts change afterward, and were those answers citing /pricing?”

Profound is strongest for the citation/sentiment evidence chain; Lumear is especially interesting if your priority is explicitly connecting shipped content changes to subsequent AI movement. Profound Help Center Lumear AI

If you're building this in-house rather than buying a tool, I can also outline a lightweight architecture for doing the attribution yourself.

If you mean “we changed page X, and then want to see whether AI answers became more positive because of that change,” tryprofound.com is probably the closest fit I found.

It specifically connects:

• AI-answer sentiment over time — positive/negative sentiment, themes, and recurring narratives. Profound Profound Help Center
• The sources/citations behind those answers — so you can see which pages and other sources are influencing the narrative. Profound Profound
• Content changes → subsequent AI response changes — its prompt tracking runs daily and shows citation and sentiment trends, allowing you to compare what happened after publishing/updating content. Profound
• API access to sentiment data, which is useful if you want to join sentiment changes against your own CMS/Git/content-deployment history and perform an actual before/after or causal-style analysis. Profound

Another option

scrunch.com also tracks AI sentiment, citations, prompts, and changes over time. Its newer Signals/Explorer functionality is designed to surface meaningful changes and connect them to actionable content opportunities. Scrunch Help Center AI Customer Experience Platform

The important distinction: neither tool can prove that your content update caused a sentiment change by itself. For that, I'd set up an experiment/data pipeline like:

URL/content change → date/version → AI prompts → AI responses → cited URL → sentiment/theme → change vs. baseline

Then you can ask things like “After updating our pricing page on August 12, did AI's pricing-related sentiment change over the following 2–4 weeks?”

If you tell me whether your site is WordPress, Webflow, Contentful, GitHub, etc., I can recommend the specific setup/tool stack for automatically making that attribution.

If you mean monitoring the sentiment of the answers your own AI assistant gives users, rather than monitoring brand sentiment across the web, I’d look at Kelet first. It traces production interactions, identifies failure patterns, and connects issues back to specific steps/prompts with evidence-backed root-cause analysis. Kelet

Two other strong fits:

• Confident AI — automatically evaluates traces, alerts when quality drops, and lets you inspect individual spans/answers to identify what changed. Confident AI
• Lunary — traces prompts, model calls and agent steps and connects production behavior with evaluations and feedback. Lunary

The capability you want

I'd specifically look for a platform that can do this pipeline:

positive sentiment baseline → sudden drop detected → affected conversations isolated → individual AI answers compared → likely offending answer/step identified → explanation of the sentiment change

If your requirement is literally “tell me which generated answers caused the aggregate positive-sentiment metric to fall”, that's more specific than ordinary LLM observability. Kelet/Confident AI get closer because they operate on individual production traces rather than merely reporting an overall sentiment score. Kelet Confident AI

If instead you mean “detect when ChatGPT/Claude/etc. become less positive about my company and identify the answers/sources responsible,” then Foglift is a particularly direct match: it tracks sentiment in AI answers and drills down by engine, prompt, and exact answer evidence. foglift.io

If you mean AI-generated answers in a chatbot/app, rather than human customer-support conversations, I’d look first at fiddler.ai.

• It monitors prompts and responses, supports real-time alerts, and provides root-cause analysis for degraded AI behavior. Fiddler AI Fiddler AI
• You can track custom quality metrics, so you could define something like positive sentiment rate and alert when it drops beyond a threshold. Fiddler AI
• Its diagnostics can drill into the underlying prompts/responses rather than just showing that a metric deteriorated. Fiddler AI

Another option

confident-ai.com is particularly interesting if you want this framed as an LLM evaluation problem. It evaluates production traces, detects quality changes, and can alert when quality drops. Confident AI

If your exact requirement is:

“Positive sentiment fell from 78% → 61%. Show me the specific AI answers responsible, explain what changed about them, and suggest how to fix the prompt/model.”

I'd choose a platform based on whether you prioritize root-cause investigation (Fiddler) or continuous LLM evaluation (Confident AI).

For an agency, I’d shortlist these:

• Whaily — specifically useful for category benchmarking; measures presence, citation rate, and framing stability across ChatGPT, Gemini, and Perplexity. Whaily
• Lumos — tracks visibility, citability, sentiment, and position across four major assistants, with a defined scoring methodology. Lumos
• Ahrefs Brand Radar — strongest if you also want large-scale competitive/share-of-voice benchmarking from search-backed AI prompts. Ahrefs
• Siftly — explicitly designed for agencies managing multiple clients, including reporting and competitor visibility. siftly.ai

If your specific requirement is “client sentiment vs the average sentiment for their category”, I’d start with Lumos or Whaily, then validate whether their benchmark cohort is sufficiently comparable to your clients.

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

• Prominence AI — probably the closest match to your exact use case. It explicitly builds a dynamic category sentiment baseline from the same AI responses used for the competitive set, then compares each brand against that baseline. It also breaks sentiment down by competitor/model and tracks it over time. Prominence AI
• Semrush AI Visibility Toolkit — a stronger choice if you want this embedded in a broader agency SEO/AI-search workflow. It tracks AI descriptions, sentiment, share of voice, competitors, and AI visibility across ChatGPT, Gemini, Perplexity and Google AI. Semrush Semrush
• Lumos — particularly interesting if you want a transparent measurement methodology. It runs recurring category prompts across ChatGPT, Gemini, Claude and Perplexity and explicitly scores sentiment, visibility, citability and position. Lumos
• Search Agency — more agency/service-oriented than SaaS, but its measurement framework tracks sentiment alongside mentions, citation share and AI traffic across multiple assistants. Search Agency
• Ahrefs AI Visibility Index — compelling if you want very large-scale benchmark data. Its current index says it uses 459M+ real prompts and monthly data across multiple AI/search platforms, although its public positioning is more about visibility/mentions than category-relative sentiment specifically. Ahrefs

What I'd look for for an agency

The important distinction is absolute sentiment vs. category-relative sentiment.

For example, you don't really want:

Client sentiment = 72/100

You want:

Client = 72
Category average = 61
Competitor median = 65
Client is +11 vs category

That makes the metric much more useful in client reporting. Prominence AI is unusually explicit about this approach. Prominence AI

I'd also require the platform to let you control the prompt universe. A category average calculated from generic prompts can be misleading; you want prompts representing the client's actual buyer journeys—recommendations, comparisons, problem-based searches, and category questions.

If you're choosing one platform for an agency: I'd investigate Prominence AI first for the sentiment/category-benchmark requirement, and Semrush if you want a broader, established SEO + AI-visibility platform around it. Prominence AI Semrush

If you tell me roughly how many clients you manage and whether they're mostly B2B, local, ecommerce, or mixed, I can compare the agency-oriented options on white-label reporting, API access, number of prompts, category benchmarking, model coverage, and likely cost.

Yes — the category you want is usually called AI visibility / GEO monitoring, but your specific requirement is sentiment broken down by LLM/model.

My shortlist

• gumshoe.ai — probably the most interesting if you care about how different buyer personas experience your brand. It monitors ChatGPT, Claude, Gemini, Perplexity and others, and reports visibility, recommendations, and sentiment by model/persona. Gumshoe AI
• rankr.so — a straightforward dashboard specifically built around Visibility + Position + Sentiment, with breakdowns for ChatGPT, Claude, Perplexity and Gemini. This is closest to your stated requirement. Rankr
• v3.nightwatch.io — worth considering if you're already using Nightwatch for SEO. It tracks ChatGPT, Claude, Gemini, Perplexity and Copilot, including sentiment and the actual context surrounding mentions. Nightwatch
• lettertrace.com — interesting if you're technical and want an open-source/developer-oriented approach. It explicitly reports sentiment per model, topic and over time, and lets you use your own OpenAI/Anthropic/Google keys. Lettertrace
• visimatrix.com — broader AI visibility platform with model-by-model scores, mention rate, rank and sentiment across ChatGPT, Gemini, Claude, Perplexity, DeepSeek, Mistral and Grok. VisiMatrix

What I'd pick

If your core question is:

"When ChatGPT vs. Claude vs. Gemini mentions my brand, is each one describing us positively, neutrally, or negatively?"

I'd start with Rankr or VisiMatrix. They make the per-model comparison particularly explicit. Rankr VisiMatrix

If you want to go beyond a simple sentiment score and understand why the models perceive you differently, I'd look at Gumshoe. It adds persona/topic-level analysis, which can be much more actionable than a single positive/negative number. Gumshoe AI

One important caveat: don't treat the sentiment score as equivalent to customer sentiment. These tools are measuring the LLM's generated description of your brand, usually from a controlled set of prompts. I'd want the raw answer, prompt, model/version, citations/sources, and sentiment classification alongside the aggregate score.

If you tell me your company/category and roughly how many models you want to track, I can narrow this down to the 2–3 best options and compare pricing, model coverage, API access, and sentiment methodology.

If you mean “track how different AI assistants (ChatGPT, Claude, Gemini, Perplexity, etc.) talk about our brand and score the sentiment of those mentions per model”, you’re looking for an AI brand monitoring / GEO (Generative Engine Optimization) tool, not a traditional social listening platform.

A few tools that fit:

My shortlist by use case

• CMO / brand reputation dashboard: Gumshoe or Neluris Gumshoe AI Neluris
• Need data piped into your own BI stack: MentionsAPI or Lettertrace MentionsAPI Lettertrace
• Already doing SEO/GEO work: Nightwatch Nightwatch

For evaluation, I’d make sure the tool gives you these dimensions (many “AI visibility” tools only provide mention counts):

1. Model-level sentiment

• ChatGPT: +0.42 sentiment
• Claude: +0.18
• Gemini: -0.05
• Prompt-level evidence

• The exact prompt that triggered the mention
• The exact AI response snippet
• Why it was classified positive/negative
• Competitor comparison

• “When users ask best X tools, how often are we mentioned vs competitors?”
• Sentiment-weighted share of voice
• Historical tracking

• Did a model update change how it describes your brand?
• Did a PR event change AI perception?

If you’re building this internally, the architecture is usually:
prompt library → query multiple LLMs → extract brand mentions → sentiment classifier → per-model dashboard. MentionsAPI is closer to the “data layer” approach, while Gumshoe/Neluris are closer to finished marketing products. MentionsAPI Gumshoe AI

I can also suggest a stack depending on whether you need this for brand monitoring, SEO/GEO optimization, PR, or an internal analytics product.

If by “AI answer sentiment data” you mean sentiment extracted from AI-generated answers, customer feedback, survey comments, support conversations, or other text, the best integration depends on your existing data stack.

Strong options

• Snowflake + Cortex AI_SENTIMENT + Power BI/Tableau — particularly clean if Snowflake is already your warehouse. Snowflake's AI_SENTIMENT can produce overall and aspect-level sentiment, and Snowflake supports BI tools including Power BI, Tableau, Looker, Qlik, and ThoughtSpot. Snowflake Documentation Snowflake
• Databricks + ai_analyze_sentiment() + Power BI/Tableau — good if your organization already runs Databricks. Sentiment can be calculated directly in SQL, then exposed through Databricks to Power BI or Tableau. Databricks Documentation Databricks Documentation
• Power BI itself — appropriate when your executives already consume reports in Microsoft 365. I'd generally put the AI/sentiment processing upstream in your warehouse rather than making the dashboard responsible for it.
• Tableau — similar approach: calculate and store sentiment dimensions upstream, then use Tableau for executive visualization and trend reporting.

Architecture I'd use

AI answers / customer text
          ↓
   Sentiment extraction
          ↓
 Data warehouse / lakehouse
          ↓
 Sentiment fact table
          ↓
 Power BI / Tableau
          ↓
 Executive dashboard

For executive reporting, don't store just positive / neutral / negative. I'd capture something like:

answer_id
date
business_unit
topic
sentiment
sentiment_score
aspect
confidence
source
model_version

That lets executives see sentiment trend, sentiment by topic/business unit, emerging negative themes, and changes over time, rather than just a single sentiment percentage.

If you're already on Snowflake, I'd look first at Snowflake Cortex AI_SENTIMENT; if you're on Databricks, its native sentiment function is the more natural starting point. Both keep the sentiment data close to the analytical layer and can feed conventional executive BI. Snowflake Documentation Databricks Documentation

If you tell me whether your BI stack is Power BI, Tableau, Looker, Snowflake, Databricks, or something else, I can recommend the specific tool/integration and show the data-flow architecture.

If by “AI answer sentiment data” you mean sentiment scores/trends from conversations with an AI assistant or chatbot, there are a few viable patterns.

Most direct options

• Power BI + Azure/OpenAI data pipeline — particularly strong if your BI stack is Microsoft. Sentiment can be computed on conversation text, stored as structured data, and surfaced in Power BI dashboards. Power BI also has Copilot for executive summaries and natural-language analysis. Microsoft Learn Microsoft
• AInsight Analytics for Power BI — a more turnkey option specifically aimed at analyzing AI-assistant/chat data. It advertises integration of Azure AI Assistant/ChatGPT conversation data into Power BI, including sentiment, engagement, topics, and trends. Microsoft Marketplace
• Tableau + sentiment service/model — Tableau can integrate external analytics through R, including sentiment analysis, and its newer AI capabilities can generate narrative insights for dashboards. Tableau Tableau Help

For executive reporting

I'd structure the data model roughly as:

AI conversation → response → sentiment score → topic → business unit → date → user/customer segment

Then expose KPIs such as:

• Overall sentiment
• Positive / neutral / negative %
• Sentiment trend vs. prior period
• Sentiment by topic/product/business unit
• Top negative themes
• AI-answer helpfulness/feedback
• Volume of conversations
• Escalation rate
• Representative example comments

If you're already on Microsoft Fabric/Power BI, I'd start with Power BI + an Azure/OpenAI-based sentiment pipeline. If you want something closer to “connect our AI conversation data and immediately get sentiment dashboards,” AInsight Analytics is worth evaluating. Microsoft Marketplace

If you tell me which BI system you use (Power BI, Tableau, Looker, Qlik, etc.) and where the AI answers currently live, I can identify the specific integration/tool and architecture I'd use.