# How to Address Brand Reputation Issues in AI Answers

Improving AI reputation is not only about requesting removal. Brands need reliable, clear, and continuously accessible sources of truth.

## Key takeaway
AI reputation work is not the removal of every negative statement. First separate factual errors, outdated information, and legitimate opinion, then improve evidence for the parts that can be corrected.

## Classify the problem first

Factual errors can be corrected with official records and authoritative sources. Outdated information requires updates across owned and external profiles followed by recrawling. Subjective criticism should be addressed through its evidence, not simply declared false.

- Capture the original question, full answer, platform, date, and citations.
- Mark the exact incorrect sentence instead of storing only a sentiment label.
- Identify whether the error came from the official site, an external source, or model inference.

## Build maintainable sources of truth

Create stable pages for identity, specifications, credentials, service scope, and disputed facts. Each conclusion should trace back to an original record, public filing, or authorized case. Keep structured data and important external profiles aligned when facts change.

Do not publish a wave of near-duplicate positive articles to bury a problem. That increases repetition and can make important facts harder to distinguish.


## Retest a fixed risk set

Group high-risk questions, brand comparisons, and common customer questions, then retest them on a schedule. Record whether errors disappear, citations change, and framing becomes more accurate. Keep unresolved items visible.
## Method and scope
A governance log should retain the original answer, evidence, corrective action, retest result, and owner so every change remains traceable.

## Limitations
A company cannot control every model output and should not suppress legitimate criticism. The goal is factual accuracy and stronger evidence, not uniformly positive answers.

## FAQ
### How should a team use this guide?
A governance log should retain the original answer, evidence, corrective action, retest result, and owner so every change remains traceable.

### What should a team avoid concluding from it?
A company cannot control every model output and should not suppress legitimate criticism. The goal is factual accuracy and stronger evidence, not uniformly positive answers.

## Sources
- [Google: Helpful content and trust](https://developers.google.com/search/docs/fundamentals/creating-helpful-content)

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- Canonical page: https://eraso.cn/en/insights/ai-reputation-repair/
- Brand facts: https://eraso.cn/en/brand/
- LLM index: https://eraso.cn/en/llms.txt