Each sampled answer becomes a claim-level record of what is supported, contradicted, omitted, dated incorrectly or still unresolved.

Sampled answers stay attached to their full context while the checkable claims and their source passages are pulled out. Trained reviewers decide support, materiality, severity and the appropriate escalation path. For the documented sample, you get a claim-level scorecard that separates supported, contradicted, outdated, disputed and unverifiable brand facts. Brand, communications and legal-adjacent leads deciding whether an AI answer contains a material error that warrants action.

Some of the 500+ brands we've worked with

See all references

The full answer remains available while its claims and source passages are reviewed. Materiality, dispute status, severity and escalation all pass through named human owners.

How we hold ourselves to it

Atomic claims over vibes
Tone judged apart from facts
Criticism stays if supported
Severity before escalation

Define the claim and sentiment rubric
The rubric names the canonical fact set, disputed fields, materiality rules, sentiment labels and the cases that must escalate. Fact, legal and measurement owners approve the reference set and escalation boundaries.
Fact and framing rubric with examples, exclusions and escalation thresholds.
Decompose answers into atomic claims
Complete answers stay intact while each fact, qualification, comparison and opinion receives its own reviewable record. A reviewer validates segmentation samples and removes extraction artifacts.
Answer-claim corpus linked to full run context.
Match claims to trusted evidence
Each claim is checked against an approved source, then assigned one of the rubric's evidence states. The accountable fact owner approves truth status or marks the claim disputed.
Claim-to-source matrix with evidence and reviewer rationale.
Run calibrated automated classification
The calibrated run flags omissions or framing that may materially change interpretation, while legitimate criticism and uncertainty remain in the record. The calibrated reviewers settle materiality and sentiment labels, including abstentions.
Material omission and framing register with severity.
Escalate material cases to human review
Validated issues enter the queue according to harm, persistence, audience, source pattern and the owner able to act. Legal, safety, crisis and business owners approve priority and response path in their remit.
Escalation queue with correction path and response deadline.
Trace likely sources and retest corrections
Approved source changes trigger a replay of the affected prompts, plus cases withheld from the original run, under the original closure rule. An independent reviewer closes, reopens or escalates the issue under the original rule.
Before/after accuracy report with remaining variance and closure evidence.

Reviewers receive every material claim with its status, supporting evidence, accountable owner and closure test.

Answer accuracy scorecard
A sampled claim ledger assigns every answerable brand fact its approved evidence state.
Material misrepresentation report
The material errors, omissions and misleading frames ranked by harm, persistence and audience.
Source correction priority map
Exact source, page or fact-governance changes tied to each remediable issue and its approval owner.
Human-reviewed retest pack
Post-change evidence from the repeated prompts and the withheld cases, showing which representation issues closed, which persisted, and which are new.
Supported atomic-fact rate
Use unsupported cases to correct sources or investigate. Do not average disputed facts into certainty.

A sampled answer belongs here when a fact may be wrong or stale, a necessary condition is missing, or otherwise supported wording creates a materially misleading impression. A named person makes the decision in every case.

A good fit when

AI answers repeat wrong or stale facts — Repeated samples show materially incorrect product, company, people, location or policy details.
The wording is true but changes the likely reading — A missing condition or unbalanced comparison makes the supported claim materially misleading.
An automated sentiment score is driving action — No source comparison, reviewer agreement or escalation threshold makes the label usable.
One answer bundles several checkable facts — Names, dates, features, eligibility, prices, locations and comparisons cannot share one accuracy verdict.
The claim set mixes errors with unresolved facts — Approved sources cannot yet separate supported, outdated, disputed and unverifiable statements.
Tone is changing how a supported fact is read — A missing qualification or comparison matters enough to alter the reader's decision.
Tone and factual accuracy share one verdict — The rubric cannot separate positive, negative, mixed or uncertain framing from source support.

The situation before this work starts, drawn as a Zeo scene

Better handled as other work when

You want supported criticism treated as an error — It keeps that status, and we do not pressure publishers or invent positive evidence for sentiment.
You need an automated label to settle truth — Sentiment and factual support need a calibrated human sample and an uncertain state.

GEO work starts with recording what AI answers actually say about a brand today, then moves to the parts you can influence. The consultants below work on the specific capability this page covers.

Profound
keeps the full context attached to a sampled answer, not just the flagged line
Peec AI
retains the full answer with its run conditions, which the claim record is decomposed from
Otterly.AI
tracks brand sentiment as its own signal, kept apart from whether a claim is factually wrong
OpenAI
the platform the sampled answers come from, rerun unchanged for the closure retest
Google Search Console
confirms the source page behind a claim is actually indexed and current
Airtable
the reviewer's structured record, one row per claim with its own severity and escalation path

Sampled answers and the approved sources for disputed facts are enough to begin. We'll build the claim record, isolate material errors and show which cases are ready for correction or retest.

Check disputed AI claims

How do you decide whether an AI answer is factually wrong?

We keep the full answer, then split it into checkable claims. Each claim is compared with an approved, dated source. The result may be supported, contradicted, outdated, unverifiable or disputed. An automated confidence score does not settle truth.

Is negative sentiment automatically a problem?

Supported criticism may be accurate and useful. Action begins when a fact is wrong, required context is materially absent, or the framing changes the likely interpretation. Tone alone does not trigger correction.

Can Zeo correct a false statement inside an AI platform?

We can correct controlled sources and document external errors, but we cannot force a third-party model to update or repeat a specific answer.

What counts as closing a critical issue?

The fact source and owner are corrected where appropriate, affected prompts are rerun, the cases we withheld get checked, and an independent reviewer confirms the material error no longer recurs beyond the defined range.

By team

By industry

By tool

Answer Accuracy & Claim Monitoring

Evidence first, then the human decision

Define the claim and sentiment rubric

Decompose answers into atomic claims

Match claims to trusted evidence

Run calibrated automated classification

Escalate material cases to human review

Trace likely sources and retest corrections

The record reviewers hand back

Answer accuracy scorecard

Material misrepresentation report

Source correction priority map

Human-reviewed retest pack

Supported atomic-fact rate