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AI Brand Sentiment Analysis

Measure how AI engines describe your brand

AI search visibility tells you whether you appear. Sentiment analysis tells you how AI engines talk about you, criterion by criterion, vs every competitor in your category. Surface bias, misinformation, and hallucinations the moment they emerge.

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ChatGPTChatGPT
ClaudeClaude
GeminiGemini
PerplexityPerplexity
DeepSeekDeepSeek
GrokGrok

The problem

Visibility is not the same as a positive answer.

Your brand can be cited in every AI response and still lose deals because the model frames you as expensive, hard to integrate, or weak on support. Worse, generative engines hallucinate features, invent pricing, and recycle outdated reviews as if they were current. Without per-criterion sentiment tracking, every prospect conversation that starts with an AI search is a black box.

How it works

From sign-up to signal in minutes.

1

Configure the purchase criteria that matter in your category

Whaily proposes the criteria buyers in your industry actually use, drawn from the AI responses themselves: pricing, integrations, support quality, security posture, performance, onboarding, vertical fit, and so on. Edit the list, add your own, and the rest happens automatically.

2

We score sentiment per criterion across every model and competitor

On every prompt run, Whaily extracts the claims each AI model makes about you and each tracked competitor for each criterion. Claims become structured scores (0 to 10), aggregated across runs, and visualised as a side-by-side grid so the deltas are obvious.

3

Drill into bias, misinformation, and hallucinations

Click any cell to see the source quotes, the responses they came from, and the models that produced them. Flag claims that are factually wrong, contradicted by your own documentation, or invented entirely. The audit trail is your evidence when you escalate to a model provider or update your category content.

What you get

Everything you need, in one place.

Per-criterion scoring

Every purchase criterion gets a 0-10 sentiment score per brand per model. Compare on price, integrations, support, security, and the dimensions specific to your category.

Side-by-side competitor comparison

See your scores next to every tracked competitor in the same view. The cells where they outscore you are the cells where you are losing the AI-mediated buyer.

Bias detection

Spot when a model is consistently negative on you and consistently positive on a competitor for the same criterion. That is bias, not noise, and it is correctable with the right content.

Hallucination flagging

Mark factually wrong claims (made-up features, invented pricing, contradicted facts) so you can track misinformation per model over time and escalate when it matters.

Sentiment trend over time

Watch each criterion score shift week over week. A content push, a launch, or a press cycle either moves the needle or it does not, and now you can see which.

Source drill-down

Every cell is a click into the exact response excerpts that produced the score. Pin the worst quotes, share them with the team, and turn them into the next content brief.

Purchase criteria grid: you vs the market, in one view

The flagship view. Rows are the purchase criteria buyers in your category care about. Columns are you and every tracked competitor. Each cell shows the sentiment score and a one-line summary of what AI says about you on that dimension. Switch the active model in the tab strip to see how each engine differs.

Screenshot slot

Purchase criteria grid from /app/competitors with 5-7 rows of criteria and 4 brand columns. Highlight one cell where the user is winning and one where they are losing.

/app/competitors

Why it matters

The G2 criterion AI buyers now expect.

AI brand sentiment analysis is the discipline of evaluating how AI platforms interpret and present brand-related information, detecting biases, misinformation, and hallucinations. It is now a buyer-side requirement, not a nice-to-have. When prospects compare vendors through ChatGPT, Gemini, or Perplexity, the sentiment of the answer shapes the shortlist before a salesperson sees a lead.

Whaily was built around this measurement. Every AI response we capture for your tracked prompts is decomposed into per-criterion claims. A sentence like "Vendor A has solid integrations but pricing is opaque" yields one positive score on integrations and one negative score on pricing, with the source quote attached. The aggregation runs across every model you track, every prompt you care about, and every competitor in your category, so you see the full picture, not a single anecdote.

The same pipeline surfaces hallucinations and misinformation as a side effect. When a model invents a feature you do not ship or quotes a price you have never charged, that is a flagged claim in the audit log. You decide whether to correct your own content, update third-party listings, or escalate to the model provider. Either way, you have the evidence and the timeline.

Per-criterion drill-down with the source quotes

Click any cell to expand the underlying claims. See the exact AI response excerpts, the models that produced them, and the date stamps. Flag a claim as misinformation or hallucination in two clicks. The audit log is your record for when you escalate.

Screenshot slot

Drill-down panel showing 3-5 quoted excerpts per AI model with their score contribution and a "Flag as hallucination" affordance.

/app/competitors

Questions

The short answers.

What is AI brand sentiment analysis?+
AI brand sentiment analysis evaluates how generative AI platforms interpret and present information about your brand. It measures the tone and substance of every AI claim about you, scores those claims per purchase criterion, and surfaces biases, misinformation, and hallucinations. Whaily runs this analysis automatically for every tracked prompt across every major AI engine.
How is this different from generic sentiment analysis on social media?+
Social sentiment looks at posts and reviews on platforms you can crawl. AI sentiment looks at the answers generative engines like ChatGPT, Gemini, Claude, and Perplexity produce when prospects research your category. The audience is different (active buyers, not casual mentions), the signal is structured (per-criterion scores, not free text), and the surface is the one increasingly shaping shortlists before any human conversation happens.
How do you detect AI hallucinations about my brand?+
When a model produces a claim about your brand, Whaily attaches the source quote and stores it. Your team flags claims that contradict your documentation, invent features you do not ship, or cite pricing you have never published. Flagged claims are tracked per model, per criterion, and per prompt over time, so you see both the rate and the direction of misinformation, not just the existence.
What are purchase criteria and where do they come from?+
Purchase criteria are the dimensions buyers in your category use to evaluate vendors: pricing, integrations, security, support quality, ease of use, performance, vertical fit, and so on. Whaily proposes a starter list specific to your industry by analysing the AI responses we already capture, then you edit, add, or remove criteria so the grid matches how your buyers actually think.
Can I see sentiment per AI model?+
Yes. The criteria grid has a model tab at the top so you can switch between ChatGPT, Gemini, Claude, Perplexity, DeepSeek, and any other engine you track. A model that talks about you positively on price but negatively on integrations is a different problem from a model that is consistently negative across the board.
How quickly does sentiment analysis update?+
Sentiment is recomputed on every prompt run. A daily-cadence prompt produces a fresh score every 24 hours; an hourly prompt updates hourly. Trend lines per criterion show whether your last content push, launch, or press cycle actually moved the score.

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