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COMPANYPROOF / AI COMPANY FACT CHECKING

An AI fact checker. Built for company information.

If ChatGPT has made up facts about your company, keep the original answer, resolve the legal entity and check each material claim against evidence. Separate supported facts, corrections and unresolved questions before the answer reaches a buyer or business workflow.

Check a company answer Discuss an AI workflow
Registry evidence REST API & MCP Claim monitoring
AN ANSWER UNDER REVIEWIllustrative example

Select a statement to inspect what supports it.

02 / Incorporation

A date that needs correcting

What the answer says
15 January 2021
What the example evidence says
15 January 2020

Preserve the original statement and return the observed date beside its evidence.

Explore supported verification fields
Read the reviewed answer

“The company is active. It was incorporated on 15 January 2020. The proposed group relationship needs further evidence.”

Illustrative revision. Preserve source context and check time with each supported statement.

THE BUSINESS QUESTION

ChatGPT made up facts about my company. What should I do?

Keep the original answer and the prompt that produced it. Identify the legal company by country and registration number, then split the response into material claims rather than asking the same model to confirm the whole paragraph.

Compare each claim with evidence that covers the right entity and date. Return a supported fact, a correction or an unresolved question. Revenue also needs the reporting period, currency, units and group or subsidiary scope; ownership needs a visible relationship path.

CompanyProof checks factual company assertions regardless of who wrote them. It does not detect whether text was AI-generated, certify an entire answer as true or turn missing evidence into proof that a claim is false.

FROM FLUENT TEXT TO FACTUAL CLAIMS

Four ways a company answer can go wrong.

A useful AI fact checker makes the failure specific. Review the identity, the statement and the supporting record together.

AI ANSWER RELEASE GATE · ILLUSTRATIVE

Build a release gate for company claims.

Choose the statement type and record which context survived generation. The map shows the evidence path or review boundary—not whether the claim is true.

1. Claim in the answer
2. Context available
Verification pathDeterministic identity check

Registered name, registration number, status, incorporation date, VAT or tax ID and legal form can use the supported claim-verification path.

  1. 01Resolve entityIdentified
  2. 02Check claimPath selected
  3. 03Release or holdReady for an evidence check
Next actionReady for an evidence check

Compare the submitted value with the resolved company record and retain the returned evidence.

No citation survived? Retrieve independent evidence before release; another model answer is not a source.

Open the relevant CompanyProof path

Illustrative workflow only. This is not a truth verdict, confidence score, risk score or legal conclusion. A cited source still needs to support the exact claim.

01ENTITY CONFUSION

The facts belong to another company.

Similar names, brands, subsidiaries and operating entities can be mixed into one answer. A model may combine the address of one business with the incorporation date or revenue of another.

How to investigate

Resolve the intended company using its jurisdiction and registration number where available. Keep that entity identifier attached to every subsequent claim and profile request.

  • Distinguish a brand from the registered company.
  • Confirm parent and subsidiary relationships.
  • Ask for missing country or identifier details.
02STALE INFORMATION

The statement was once true.

A company’s status, officers or relationships can change after a source was published. A current-looking answer may still rely on an older record, especially when the text does not preserve its verification time.

How to investigate

Compare the assertion with available records and retain both the check time and the source’s own date when supplied. Treat those timestamps as different pieces of information.

  • Keep historical claims tied to their stated date.
  • Correct a present-tense claim when current evidence differs.
  • Monitor eligible claims that remain in use.
03NUMBERS WITHOUT CONTEXT

The figure has lost its meaning.

Revenue can look plausible while describing the wrong financial year, currency, units or consolidation scope. A value copied from a group report should not silently become a fact about a single subsidiary.

How to investigate

Inspect the available financial record before comparing the number. Preserve the reporting entity, period, currency, units and individual or consolidated scope with the result.

  • Distinguish reported results from forecasts.
  • Compare like accounting periods and scope.
  • Keep financial review separate from a basic identity verdict.
04MISSING EVIDENCE

A confident sentence goes beyond the source.

A profile may list shareholders without establishing an ultimate owner. A source may describe a company’s activity without supporting a claim about its market position, solvency or trustworthiness.

How to investigate

Identify exactly what the record supports. Split compound sentences into individual assertions and leave an unanswered question visible when a claim cannot be established.

  • Separate facts, interpretations and predictions.
  • Require support for each material assertion.
  • Do not equate missing evidence with proof of hallucination.

CONNECT THE STEPS

Check an answer before it travels further.

Keep the original wording and context. The purpose is to make each company claim reviewable, including the statements that need correction or further investigation.

  1. 01

    Submit the original text

    Paste the company answer, preserving dates, numbers, negation and qualifications. Remove unrelated personal information or secrets before submitting it.

    OUTPUTThe original company assertions
  2. 02

    Confirm the company

    Provide the company and country when missing. Clarify ambiguous names so the evidence relates to the intended legal entity.

    OUTPUTA resolved business identity
  3. 03

    Review each claim

    Inspect supported facts, contradictions and unresolved questions. Use the source name, available link and relevant time context to investigate the result.

    OUTPUTClaim-level outcomes and evidence
  4. 04

    Correct and retain

    Update the answer where the evidence requires it. Save the record and monitor eligible facts that continue to support a published answer or operational review.

    OUTPUTAn answer with a review trail

HOW TO FACT CHECK AI RESPONSES

Turn one AI answer into a correction record.

A citation is a starting point, not permission to release the whole paragraph. Preserve the original answer, then decide what each material statement can actually say.

Fictional worked example · not a CompanyProof result

Generated answer: “Cedar Research Ltd is active, earned €12m in 2025 and is the market leader.”

Assumed evidence: the resolved entity’s status is active; a €12m figure belongs to its parent’s consolidated 2024 report; no evidence establishes market leadership. These invented inputs demonstrate the method, not coverage or accuracy.

Three assertions, three separate release decisions. The decisions below are review instructions—not API verdict names.
Original claimEvidence checkWhat the answer can retain
“is active”Match the legal entity and jurisdiction, compare status and retain the check time. Supported identity fields →“The company’s status was active when checked.” Attach the returned evidence and time; this is not a solvency or trustworthiness conclusion.
“earned €12m in 2025”The assumed record covers another entity, period and scope. Financial context reference →Remove the subsidiary revenue claim. If relevant, describe the parent’s 2024 figure with its currency, units and consolidated scope. Otherwise leave revenue unresolved.
“is the market leader”No defined market, comparison or supporting evidence. A company record alone cannot establish the conclusion.Remove or qualify the statement under your editorial policy. Do not label it false solely because evidence is missing.

Keep a review trail outside the generated prose.

Retain the prompt and answer version, legal entity, exact claim, observed value, source and source date when supplied, retrieval/check time, correction, unresolved gap and review owner. These are suggested review-record labels, not a new CompanyProof API schema. Use the documented verification response for actual fields and verdicts.

Before release, handle unsupported fields and request errors explicitly. For eligible monitored claims, a confirmed source change can reopen the answer; an unavailable recheck is not proof of change. Read the monitoring contract →

Will this change what ChatGPT says about my company?

Checking a saved answer or correcting your own application does not update another provider’s model, erase an earlier answer or guarantee future answers. Keep the supporting evidence, correct the surfaces you control and use the relevant provider’s correction or feedback process where appropriate. CompanyProof does not promise search visibility or AI citations.

Try the identity-verification sandbox →Connect a compatible AI client →Test agent behaviour separately →

A COMMON COMPANY QUESTION

Four steps to correct an AI answer about your company.

A generated answer can mix a trading name with a different legal entity, reuse an older record or fill a missing detail with a plausible statement. Correct the evidence chain, not only the wording.

  • Save the exact prompt, answer, model or product and check time.
  • Resolve the legal entity using its country and registration number where available.
  • Split the answer into claims and check each source against the correct entity, period and scope.
  • Publish supported facts and corrections; keep unresolved claims visible and monitor eligible facts that remain in use.
See a dated registry evidence example ↗Integrate supported claim verification ↗

GROUNDING & VERIFICATION

Ground the answer. Then check what it says.

AI grounding supplies a model with relevant information while it prepares a response. Retrieval-augmented generation, or RAG, is one way to retrieve source material and pass it into that generation step. Company records delivered through an API or MCP can provide the business context.

Fact checking examines the assertions in the resulting answer. The presence of a source document does not establish that the model used the correct company, interpreted the numbers correctly or limited every sentence to what the source supports.

Use company data to inform generation, then verify the supported claims that reach a user or another system. Retain the source context and answer version so a reviewer can reconstruct the check. This makes grounding and verification complementary parts of an enterprise AI workflow.

Company data for AI workflows ↗Connect through MCP ↗

LLM HALLUCINATION DETECTION

Make the result actionable.

LLM hallucination detection is most useful here when it identifies a specific company claim that conflicts with available evidence. A wrong incorporation date can be corrected; a claim about ownership may require a different record; an unsupported prediction should not be relabelled as a verified fact.

Keep three outcomes separate in your application: evidence supports the assertion, evidence contradicts it, or there is not enough evidence to decide. The API’s documented verdicts and the text checker’s explanations provide the detail to build those review paths. An unavailable source is a data gap, not a factual correction.

The deterministic verification endpoint covers six company-identity fields. Extended ownership, officer and financial questions use relevant profile or text-research workflows where data is available. Check coverage and the actual returned evidence before deciding which answers your application can publish automatically.

Review claim-verification outputs ↗Inspect the evidence ledger ↗Ownership and group evidence reference ↗

FROM RESEARCH TO PRODUCTION

Keep the company facts connected to your agent.

For an analyst, the workflow starts with a pasted answer. For a software product, it starts with the company identity and assertions in a request. REST provides a structured integration; MCP makes documented company-data and verification tools available to a compatible AI client.

Your application decides what happens next: present a correction, request clarification, send an exception to an analyst or update a stored record. Keep the proof reference with the answer rather than copying only the final verdict into your user interface.

Where supported claim monitoring is enabled, a later change can mark evidence stale and trigger a webhook. Define how your product reopens the affected answer, and make clear to readers when the company information was last checked.

Implementation workflows ↗Verification response reference ↗

MAKE THE RESULT REVIEWABLE

Know what each method establishes.

Choose the method for the question you need to answer. These approaches address different problems in AI-generated content.

MethodQuestion it addressesWhat to inspect
AI writing detectionWas this text likely generated by a model?An authorship estimate does not establish whether the company facts are true.
Model confidence or a second opinionDoes a model judge the answer plausible?Check whether the assessment is tied to independent source evidence.
Grounding / RAGWhat company information is available during generation?The entity match, retrieved records and the context supplied to the model.
Company fact checkingDo individual assertions agree with the available evidence?The claim, observed value, source, time and any unresolved question.
Claim monitoringHas the evidence supporting an eligible claim changed?The original proof, change event and the application’s next review step.
Official company information

Over a decade collecting and structuring official company information. Assess the source and available fields for the companies you actually use.

Explore countries & sources ↗

AN ENTERPRISE EVALUATION

Measure accuracy at claim level.

Test the company answers your product actually creates. Include wrong entities, plausible but incorrect facts, historical statements and records that are not available.

01

Claim capture

Check whether every material assertion was identified without losing dates, negation, currency, units or qualifications.

02

Entity accuracy

Measure whether the review used the intended legal company rather than a similar name or parent.

03

Evidence fit

Assess whether the source supports the exact claim, entity and period. A working citation is not automatically sufficient evidence.

04

Release policy

Define which supported claims can proceed, which corrections return to the product and which unresolved cases require a human.

YOUR COUNTRIES. YOUR COMPANY FACTS.

Decide which company claims your AI can release.

Share the company claims your product creates, the countries it covers and the decisions those answers influence. We’ll help you scope the evidence, exceptions and integration path.

Evaluate an AI workflow ↗Compare plans and usage

FREQUENTLY ASKED QUESTIONS

AI company fact checking: frequently asked questions

What is an AI fact checker for company information?

It evaluates factual statements about a business against relevant company records. CompanyProof helps resolve the company, examine individual assertions and inspect the evidence behind supported facts, corrections and questions that remain unresolved.

What should I do if ChatGPT gives incorrect information about my company?

Record the original claims, resolve the exact legal entity using its registration number and country, and compare the statements with current official records. Keep supported facts, correct contradictions and identify claims lacking evidence. A new confident answer from the same model is not an independent source.

Can I fact-check a ChatGPT answer about a company?

Yes. Paste the original answer into the CompanyProof text checker. Keep dates, financial periods and qualifications intact, and provide the company and jurisdiction if the checker needs them to resolve the identity.

Does CompanyProof detect AI-written text?

No. It checks the reliability of company information rather than the likely author of the text. The same factual review can be useful for an AI answer, supplier statement, analyst note or company profile.

How does this help with AI hallucination detection?

It makes contradictions with company evidence visible and separates them from claims that lack sufficient evidence. Missing data alone does not prove a hallucination. Review the actual assertion, selected company and supporting source before drawing a conclusion.

What is the difference between AI grounding and fact checking?

Grounding gives a model relevant information during generation. Fact checking evaluates the claims in the answer it produces. An application can use company data for grounding and then check the supported assertions before the answer is used.

Is RAG enough to prevent incorrect company answers?

Retrieval can provide useful source context, but an answer can still use the wrong entity, confuse reporting periods or add an unsupported interpretation. Claim-level review checks how the retrieved information was used in the resulting statement.

Are AI fact-checking tools reliable?

Treat them as review aids rather than final authorities. A useful tool exposes the original claim, selected entity, observed value, source and unresolved gaps. Test it with known wrong entities, dates and reporting scopes, then define which outcomes require human review.

Which company claims can be checked automatically?

The deterministic endpoint supports registered name, registration number, status, incorporation date, VAT or tax ID and legal form. Other company questions use available profile or text-research workflows and require attention to source coverage and returned evidence.

Can CompanyProof verify company revenue or ownership?

Available financial, shareholder and group records can support a review of those statements. Keep the reporting period, currency, ownership context and data limitations visible. Those records do not automatically become a six-field identity-verification verdict or a complete UBO calculation.

Can Claude or another AI agent connect to CompanyProof?

A compatible client can connect through the documented MCP interface. Use the connection steps in the API documentation, authorise the appropriate account access and check the client’s supported authentication method. REST is also available for application integrations.

Can a checked answer be monitored after publication?

Eligible live claims can be monitored while monitoring and the required account access remain active. Your application can use change events to reopen an answer or review. Define the alert owner and update policy before relying on automated follow-up.

CONTINUE YOUR REVIEW

A public evidence example

See the company identifier, observations, source and date together.

Company data for grounding

Supply your application with company profiles and source context.

Claim Verification API

Integrate supported claim verdicts and corrections.

Enterprise use cases

Explore research, compliance and credit workflows.

Prepared by CompanyProof · Updated 20 October 2026