What does it mean that AI models are citing us wrong?
When an AI engine asserts something inaccurate about your brand, the fix is diagnostic, not argumentative: AIQ identifies which source is feeding the error, then the remediation targets that source specifically, a Wikipedia edit request, a press correction, a structured-data fix, or an owned-content addition.
“Citing us wrong” has a specific operational meaning in AI reputation work: an engine is asserting something about the brand that is factually inaccurate or materially misleading, and doing so with the same confident tone it uses for true statements. The instinct is to argue with the response. The right move is diagnostic, identify the source, then route to the fix that matches it.
Step 1: Identify what the engine said and what it drew on
Note the exact assertion and, where the engine shows citations, which sources it names. Where citations are not shown, AIQ surfaces the source patterns that match the response. The goal of this step is one question: which source is feeding the error, and can it be moved?
Step 2: Route the diagnosis to the matching fix
Each kind of source error has a distinct remediation path. Once the source is identified, the intervention is usually unambiguous:
| What the engine is doing | The matching fix |
|---|---|
| Paraphrasing a Wikipedia article | Wikipedia edit request, disclosed conflict of interest, policy-compliant sourcing, filed on the article’s Talk page |
| Citing an outdated or inaccurate published article | Press correction through the outlet’s corrections process; reputable publications act on documented factual errors |
| Reading the wrong Knowledge Graph value | Structured-data fix: Wikidata property correction and, where applicable, Google Knowledge Panel feedback |
| Missing the right information entirely | Owned-content addition that puts the accurate fact into the public record where engines can retrieve it |
Step 3: Track until the correction propagates
After the source-level fix is made, monitor in AIQ across the eight engines it currently tracks until the correction appears in responses. Retrieval-heavy engines reflect updated sources more quickly; training-baselined engines update on their own retraining cycles. Ongoing monitoring confirms when the remediation is complete and flags any regression.
The work is targeted, not diffuse, once the source identification is correct. You are not trying to change the engine’s mind; you are changing the source the engine is reading.
Last reviewed: 19/05/2026
