Answers that mention Peec AI

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

Two particularly good fits:

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

My pick

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

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

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

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

1. OtterlyAI — my pick for most teams

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

For example, you can monitor:

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

and see:

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

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

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

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

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

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

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

3. Peec AI

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

What I'd choose

Small/mid-size marketing team → OtterlyAI

Enterprise / global brand → Profound

Agency managing many brands → Peec AI or Profound

Already heavily invested in SEO/Semrush → Semrush AI Visibility

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

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

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

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

My shortlist

My pick: Profound

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

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

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

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

One important caveat

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

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

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

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

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

My shortlist

Why I'd pick Profound

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

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

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

So I'd structure the measurement around:

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

rather than simply:

AI referral → conversion

If you want the most rigorous setup

I'd actually consider:

Profound + GA4 + BigQuery + your CRM

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

That lets you answer questions such as:

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

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

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

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

If your goal is specifically “Did AI answers about my brand influence website traffic and ultimately conversions?”, I’d look beyond generic sentiment-analysis software. You want a stack that connects AI-answer visibility → sentiment/recommendation → web traffic → conversion/revenue.

My top picks

Profound would be my first demo. Its current offering explicitly combines AI-answer visibility with referral/human traffic data and integrations with analytics/CDN infrastructure. Profound

If conversion attribution is the #1 requirement

I'd also seriously investigate LLM Pulse. It connects to GA4, Adobe Analytics, PostHog, Plausible and Piano, and is designed to show how visibility in ChatGPT/Perplexity/Gemini translates into visits, conversions and revenue. LLM Pulse

That's closer to the model I'd want:

AI answer
→ brand mentioned/recommended
→ sentiment/position
→ citation/source
→ AI platform
→ resulting site visit
→ landing page
→ engagement
→ signup/purchase
→ revenue

One important caveat

Don't treat AI referral traffic = AI influence.

A person may see your brand recommended by ChatGPT, later Google your brand, and then convert through Google or direct traffic. GA4 will capture the latter as another channel. Current research is finding exactly this kind of indirect effect: Scrunch reports that people exposed to an AI recommendation subsequently became substantially more likely to search for and visit the recommended brand. Scrunch

So for a sophisticated measurement program, I'd track two separate effects:

1. Direct AI attribution — “ChatGPT sent this visitor, who generated $X.”
2. AI influence/halo effect — “AI recommendation/sentiment increased branded search, direct traffic, conversion rate, or revenue.”

If you tell me B2B vs. ecommerce, your analytics stack (GA4/Adobe/HubSpot/etc.), and roughly how much traffic you have, I can narrow this to the best 2–3 platforms and give you a recommended measurement architecture.

Profound would be my first demo. Its current offering explicitly combines AI-answer visibility with referral/human traffic data and integrations with analytics/CDN infrastructure. Profound

If conversion attribution is the #1 requirement

I'd also seriously investigate LLM Pulse. It connects to GA4, Adobe Analytics, PostHog, Plausible and Piano, and is designed to show how visibility in ChatGPT/Perplexity/Gemini translates into visits, conversions and revenue. LLM Pulse

That's closer to the model I'd want:

AI answer
→ brand mentioned/recommended
→ sentiment/position
→ citation/source
→ AI platform
→ resulting site visit
→ landing page
→ engagement
→ signup/purchase
→ revenue

One important caveat

Don't treat AI referral traffic = AI influence.

A person may see your brand recommended by ChatGPT, later Google your brand, and then convert through Google or direct traffic. GA4 will capture the latter as another channel. Current research is finding exactly this kind of indirect effect: Scrunch reports that people exposed to an AI recommendation subsequently became substantially more likely to search for and visit the recommended brand.

So for a sophisticated measurement program, I'd track two separate effects:

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

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

My recommendation

For a large enterprise, I'd shortlist:

If I were designing this for an enterprise

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

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

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

The architecture I'd use

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

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

For example:

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

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

One important enterprise requirement

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

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

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

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

Sentiment: 74

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

My choice

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

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

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