Mirrorline · AI Reputation Research Prototype
Auditing what AI assistants say about an organization
When someone asks an AI assistant about an organization, the answer becomes part of its reputation surface. Yet few communications teams have a systematic way to examine how those answers vary, where they come from, or whether the underlying claims are supported.
The
problem
A growing share of first impressions now happens inside AI assistants rather than on a search results page. Those answers are generated fresh, vary by model and phrasing, and can drift from what the record supports, an outdated fact, a confused entity, a claim with no source.
Organizations monitor their press and their search results. Few have a systematic way to examine the assistant layer.
"Northwind Renewables, founded in 2009, operates the largest offshore wind farm in the North Sea."
Public filings show incorporation in 2013. No project it operates ranks in the top ten by installed capacity.
The absence of a source is itself evidence. An assistant asserting something the record can't support is exactly the signal worth surfacing. The output is a reviewed brief built for a decision-maker, not a dashboard of scores.
"The system produces signals for review, not determinations of truth. It flags, sources, and contextualises; judgment stays human."
, a divergence is not a verdict, and nobody can honestly promise control over what models say. Designed to run in French and English.
I’m always glad to exchange ideas with people working across reputation, communications and responsible AI. Connect on LinkedIn →
Personal project, in development · builds on the deterministic-gating and fail-closed patterns from the harmful-information pipelines and the executive-readout engine. Results vary by model and phrasing, one answer is not automatically a material finding, and the system surfaces signals for human review. Engineering write-up only, this page documents research and method, not a service.
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