How do you correct AI-generated misinformation about your brand?

AI engines do not generate misinformation independently, they reflect and amplify whatever their anchor sources say. Correcting what an engine says about your brand therefore starts not with the engine itself but with identifying the specific source the engine is anchored to, and then fixing or countering that source. AIQ shows this directly for retrieval-based engines (cited sources are visible in the response) and pattern-matches against likely training sources for engines that do not surface citations.

Correction workflow by source type

1. Identify the anchor source.
   Run the relevant prompts through AIQ across the eight engines AIQ currently tracks: ChatGPT, Copilot, Gemini, AI Overview, Perplexity, Grok, Claude, and Google AI Mode. For retrieval-heavy engines (Perplexity, ChatGPT Search, Gemini with Google Search grounding), the cited sources appear inline. For training-baselined engines, compare the exact phrasing of the misinformation against Wikipedia, aggregators, and major press to find the likely source.
2. If Wikipedia is the anchor: file a Talk-page edit request.
   Wikipedia is one of the most heavily weighted sources across AI engines and a primary input to Google’s Knowledge Graph. Corrections go through the standard disclosed-COI edit-request process on the article’s Talk page: quote the wrong text, propose the exact replacement, and cite reliable secondary sources. Direct edits from a conflict-of-interest account get reverted even when the underlying correction is accurate. The Talk-page process, done correctly, produces durable corrections the engines then absorb.
3. If a specific article or aggregator is the anchor: pursue a press correction or strengthen competing sources.
   Most reputable news outlets have published correction processes; documented factual errors submitted through the right channel are corrected more often than practitioners expect. Where a correction is not available, build authoritative competing sources that accurately describe the same facts, press placements in outlets the engines weight, updated owned content, until the engines re-weight away from the inaccurate anchor.
4. If a Knowledge Graph or Wikidata value is wrong: submit corrections through the appropriate channel.
   Google’s Knowledge Graph can be corrected through verified entity feedback (available once identity is verified via the Knowledge Panel claim process) and indirectly through Wikidata corrections, which flow into the Knowledge Graph. Wikidata updates propagate to AI engines faster than Wikipedia narrative changes and should be a priority for structured-data errors such as wrong founding dates, leadership, or corporate relationships.
5. Monitor propagation across the eight engines AIQ tracks.
   Once source-level corrections are in place, track daily in AIQ until each engine reflects the accurate information. Retrieval-heavy engines, those that perform live web searches when a query arrives, typically update within days of the underlying source changing. Training-baselined engines update only on retraining cycles, which may take longer. The engines do not all move at once, and monitoring across the eight engines AIQ tracks prevents false confidence from one engine’s correction while others still carry the error.

What cannot be done

No service or technical capability allows anyone, including the companies that build the engines, to edit a specific AI response directly. The work is always at the source layer. Claims from any firm about direct AI output control misrepresent what the discipline can actually do. Source-level corrections, sustained over time and monitored through AIQ, are the mechanism that produces durable results.