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AI Visibility Intelligence · Brand Sentiment

How AI feels about you, and whether it has the facts right.

Being mentioned is only good news if the tone is warm and the facts are true. Brand Sentiment scores how positively each engine portrays you, tracks that score over time, and flags the moment it sours. Alongside it, Accuracy Monitoring catches outdated, false, or unflattering claims before a buyer reads them.

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CautionWarm recommendation

Tone scored for your brand alone

Outdated claim flagged

Inaccurate claim flagged

Unflattering claim flagged

Illustrative

GrackerAI dashboard showing AEO and GEO citation tracking across ChatGPT, Perplexity, Claude, and Gemini
A cold tone and a confident mistake do the same damage.

Visibility tells you AI is talking about you. It does not tell you if that helps or hurts.

The number of mentions can hold steady while the story behind them quietly turns against you. The tone drifts cool over months with no alert. The facts go stale or wrong — old pricing, a feature you dropped, a claim that was never true — and the model states all of it with the same confidence as the truth.

The slow drift

A souring tone you never see

Sentiment rarely collapses overnight. It cools gradually, one hedge at a time, and by the time it shows up in your pipeline the narrative has already set in the models.

The blind effort

No way to know if your fixes worked

You publish, you do PR, you ship proof. Without a tone score tracked over time, you can only see whether AI mentions you more, never whether it speaks about you better.

The confident error

Wrong, and stated as fact

Engines repeat outdated and false claims about you in a calm, authoritative voice. Buyers cannot tell, and unless something is checking, neither can you.

Track not just how often AI mentions you, but how warmly and how accurately, over time, so you catch a cold turn or a wrong fact while you can still fix it.

How it reads tone and truth

One pass over every answer, judging two things at once

Every answer we capture is read for two questions in the same pass: how does it feel about you, and is what it says actually correct? Both run through the steps below, then get measured against your history and your verified facts.

Read

Take the full answer as written.

Isolate

Keep only what is said about you, not rivals.

Judge

Score the tone, and check each claim for accuracy.

Compare

Against your history and your verified profile.

Flag

Raise a souring trend or a wrong claim.

The same read answers two questions: how you are felt about, and whether it is true.

The hard part we solved

Brand sentiment in AI answers is rarely a thumbs up or a thumbs down. It hides in qualified, comparative language: "popular but pricey," "powerful, though the setup is steep," "a safe choice if you can afford it." Faint praise is not praise. So we score tone for your brand specifically, separating how an answer feels about you from how it feels about the competitor named in the same sentence, and we weigh the hedges and the comparisons instead of scanning for happy words.

Accuracy needs the opposite of mood. It needs ground truth. So we keep a verified profile of your brand — your current pricing, features, certifications, leadership and key dates — and check every claim an engine makes against it. The subtle cases are not the obvious errors. They are the claims that used to be true, and now quietly mislead.

Part one · Sentiment · Scoring the tone

Faint praise is not praise, and we score it that way

Most sentiment tools look for positive and negative words and call it a day. AI answers do not cooperate. They lean on qualifiers, trade-offs, and comparisons — the language of a careful recommendation rather than a review. "Solid but expensive" is not a compliment, and treating it like one would flatter your dashboard and mislead you.

We place each mention of your brand on a scale from cold to warm, accounting for the hedges around it, so the score reflects how a real buyer would read the line.

  • Tone scored for your brand alone, even in a shared sentence
  • Hedges and comparisons weighed, not just keywords
  • A single, readable score you can track and report
Sentiment scoring diagram showing a brand mention placed on a cold-to-warm scale, with hedges and qualifiers weighted alongside the core tone
Catching a shift

Tell a real turn from a single grumpy answer

Because the same prompt returns slightly different replies every time, one cold answer is not a trend. If you reacted to every wobble you would never sleep. So we sample repeatedly, smooth the score, and compare run over run — which means a flag only fires when the underlying tone has truly moved.

That is what lets you catch a souring story while it is still small, and just as useful, confirm that a piece of content or a PR push actually warmed the tone rather than hoping it did.

  • Run-over-run comparison, not a one-time snapshot
  • Noise smoothed out so flags mean something
  • See the effect of your fixes on how AI talks about you
Illustrative: smoothed sentiment trending down
Part two · Accuracy · Checking the claims

Catch what is wrong, what is stale, and what stings

To know a claim is wrong, you have to know what is true. GrackerAI keeps a verified profile of your brand and checks every statement an engine makes against it. Matches pass quietly. Mismatches get sorted into the kind of problem they are, because they call for different responses.

An outdated claim was once correct and needs refreshing. An inaccurate one was never true and needs correcting. An unflattering one may be accurate but damaging, and needs a decision. Each is flagged with the exact wording, the correct version, and where it appeared.

  • Every claim checked against your verified facts
  • Outdated, inaccurate and unflattering sorted apart
  • Caught the cycle it appears, before it spreads across engines
Accuracy checking diagram showing AI claims sorted into three categories — outdated, inaccurate, and unflattering — each matched against a verified brand profile
Outdated

Was once correct. Needs refreshing with the current fact.

Inaccurate

Was never true. Needs correcting at the source.

Unflattering

May be accurate but damaging. Needs a strategic decision.

How it compares

Counting mentions misses the two things that actually move a buyer.

Mention or basic sentiment toolsBrand Sentiment
Counts how often you appearScores how warmly you are described
Reads happy and sad wordsWeighs hedges, trade-offs and comparisons
Blends your tone with competitors'Scores your brand on its own
Reacts to every noisy replyFlags only a real, smoothed shift
Assumes the claims are trueChecks every claim against verified facts
Cannot spot an outdated factSeparates outdated, inaccurate and unflattering
In action

Protect the narrative, and correct the record

Two jobs that move from reacting late to catching things early.

Catch a souring narrative

A cold turn is cheap to fix early and expensive to fix once it sets. Sentiment tracking gives you the early warning.

  1. Get a flag the moment your smoothed sentiment trends down on any engine.
  2. Read the answers behind the dip to see what changed and why.
  3. Respond with content or proof that addresses the specific concern.
  4. Watch the line recover, confirming your fix actually warmed the tone.
Correct the record

When an engine states something wrong, every day it goes uncorrected is a day buyers absorb it

  1. Review flagged claims sorted into outdated, inaccurate and unflattering.
  2. See the exact wording, the correct version, and which engine showed it.
  3. Publish the corrected facts where the models are most likely to pick them up.
  4. Track the claim until the engines stop repeating it.
"AI-optimized content positioned us as experts in our niche. The results have been game changing."
+73% AI visibility
+48% business impact

Edward Zhou, Co-founder and CEO, Gopher Security

What it means for your team

Defend the story AI tells about you, on tone and on facts.

Reputation in AI search is not only about being seen. It is about being seen well, and being seen correctly. With both tracked over time, you catch the cold turns and the wrong facts while they are still cheap to fix.

8 engines your tone and accuracy are tracked across
Run over run sentiment compared each cycle, with shift flags
3 types outdated, inaccurate and unflattering claims caught
Before buyers wrong claims surfaced before they spread
Works with

Tone and truth are two layers. Here is the rest.

Brand Perception

See the specific qualities AI ties to you, like secure or enterprise, alongside the tone and accuracy of how it says them.

See Brand Perception

AI Visibility Monitoring

The answers behind every sentiment score and accuracy flag come from monitoring across up to 10 AI engines.

See AI Visibility Monitoring

Content Engine

Turn a cold tone or a wrong claim into the published proof that teaches the models a better, truer story.

See Content Engine

Do not let AI keep
recommending someone else

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