How do you handle negative AI-generated summaries of your company?

A negative AI summary is uncomfortable to read, but it is diagnostic data. The summary reflects specific sources, and identifying those sources is where the work starts. Retrieval-based engines often show their citations directly; for the rest, AIQ pattern-matches the response against the sources the engines most likely drew on. The framing almost always traces back to a small number of inputs, and the right intervention depends on which one is driving it.

Step 1 – Trace the framing to its sources

The framing usually traces to one or two specific, identifiable inputs:

• A particular paragraph in the Wikipedia article.
• A dated trade article the engines weight too heavily.
• A contentious Reddit or forum thread.
• An analyst note that ranks high in retrieval.

Step 2 – Route each source to its intervention

Once the source is identified, the fix is specific to that source:

Source driving the framing	Source-layer intervention
Wikipedia paragraph	Propose the change through the proper Talk-page edit-request process, backed by reliable secondary sourcing, for independent editors to review.
Dated or unbalanced press coverage	Pursue press corrections or strengthen counter-coverage in authoritative outlets to re-weight the source ecosystem.
Missing context	Add owned content that supplies the context the engines were missing.

Step 3 – Monitor for the correction to land

This takes weeks to months, but it works when the source diagnosis is correct. Watch the engines for the corrected framing to show up. One thing never works: arguing with the AI response itself. The engine doesn’t remember what you tell it and rebuilds each answer fresh from the sources it trusts. The response reflects those sources, so the fix has to happen at the sources.