AI Reputation Repair — When Models Repeat Wrong Facts About Your Business
When AI assistants state wrong hours, phone numbers, or services about your business, the fix is usually upstream — conflicting listings, stale directories, or thin schema — not arguing with the model. AIrecommend.ai's Accuracy Repair module traces likely sources and queues client-approved corrections across listings and entity data.
Wrong AI facts are a operations problem
A prospect says: "ChatGPT told me you're closed on Saturdays — are you?"
You're open. You lose the job anyway.
This is AI reputation damage without a traditional negative review. The model stated a false fact confidently. Arguing with the chatbot helps one user once; fixing sources helps everyone.
Common error types
| Wrong claim | Typical source |
|---|---|
| Incorrect phone | Old directory listing, acquired location data |
| Wrong hours | Stale GBP, holiday hours not updated |
| Wrong address | Suite change not synced to Apple BC / Bing |
| Services you don't offer | Aggregator keyword stuffing |
| "Permanently closed" | Duplicate GBP merge failure |
| Incorrect owner name | Legacy press or scam listing |
Models rarely invent from nothing — they compress conflicting evidence.
Step 1: Document the claim
Create an accuracy log:
- Platform (ChatGPT, Gemini, etc.)
- Exact prompt used
- Wrong statement quoted
- Date observed
- Screenshot or export
- Whether browsing was enabled
Run multiple prompts — errors may be query-specific.
Audit guide: How to check what ChatGPT says.
Step 2: Trace sources
Work outward from sources models likely ground on:
- Google Business Profile — hours, phone, categories, closures
- Apple Business Connect — often diverges from Google
- Bing Places, Yelp, Facebook — NAP drift
- Industry directories — Angi, Healthgrades, Avvo, Zillow, etc.
- Your website — footer NAP, schema, llms.txt contradictions
- Third-party pages — old articles, duplicate listings, data brokers
Search your business name + phone variants. Note every conflicting field.
Technical alignment: llms.txt, schema, robots checklist.
Step 3: Fix with approval — not panic edits
Change authoritative sources in priority order:
- GBP (owner-verified)
- Apple BC and Bing
- Website schema + llms.txt + visible NAP
- Industry directories (claim or dispute)
- Data broker opt-outs where applicable
AIrecommend.ai workflow: Accuracy Repair (Dominance module) proposes fix plans in the approval queue — you approve before we push listing updates or publish schema changes.
Step 4: Resample — do not declare victory day one
After fixes propagate:
- Re-run buyer-intent prompts monthly
- Track whether wrong claim persists
- Note if model hedges ("hours may vary") — partial improvement
No ethical vendor guarantees instant AI correction. Retrieval cadence and model versions vary.
Step 5: Strengthen corroboration
When one source is clean but AI still errs, add consistent corroboration:
- Matching NAP on 10+ directories
- FAQ schema answering hours and location explicitly
- Citable About page with dated "last updated"
- Press or studies only from real milestones (not fluff)
Dominance modules — Data Studies, Press Wire — support retrieval-heavy engines when appropriate.
Prevention
| Practice | Benefit |
|---|---|
| Quarterly NAP audit | Catches drift before models absorb it |
| Single source of truth doc | Intake for Entity Profile module |
| llms.txt "last updated" | Signals freshness to crawlers |
| Post-move checklist | GBP + Apple BC + website same day |
| Monthly AI scan | Early detection |
Free scan includes competitor and platform tables — rerun after major listing changes.
Reviews vs accuracy repair
Negative reviews are sentiment problems. Accuracy repair is fact problems. Different playbooks:
- Reviews → ethical solicitation, approved responses — Google reviews the right way
- Facts → listing/schema correction, no gating shortcuts
Platform overlap caveat
An error fixed for Gemini may linger in ChatGPT if sources differ — see 11% platform overlap. Fix broadly, measure per platform.
What we will not do
- Claim a direct "OpenAI correction API" for local businesses
- Auto-edit listings without client approval
- Publish fake press to "flood" training data
- Guarantee error-free AI answers
AIrecommend.ai Dominance tier
AI Accuracy Repair is module #8 in the Growth Engine:
- Wrong-fact monitoring from scan deltas
- Source trace reports
- Queued fixes across listings + entity
- Monthly verification rescans
Bundled with GBP Autopilot, studies, press, awards at $9,999/mo. Growth tier ($4,997/mo) includes tracking, reviews, listings, entity, Super Pixel.
Pricing · AEO services · AI visibility tracking.
Bottom line
AI reputation repair means making public evidence agree so models have nothing to garble. Measure accuracy over time, approve changes deliberately, and pair fixes with broader AEO signal work — not superstition about "prompt hacking."