Protect · 01
AI Reputation Management
What generative systems say about you when nobody is watching. An audit of the current answers, correction of the sources they draw on, and monitoring as the models change under you, which they do, without notice.
The premise of this service is uncomfortable and simple. A meaningful share of the people forming a view about you this year will not read a page of search results. They will ask a model, read a paragraph, and proceed. That paragraph is now the first impression, and it is generated from whatever material happens to be accessible and consistent.
Reactive correction, meaning fixing an answer that is already wrong, is covered in the reactive practice. This is the standing version: knowing what the answers are before someone else discovers them, and maintaining source material substantial enough that the systems have little room to improvise.
In practice, three things determine the quality of a machine answer about you. Whether authoritative material exists at all, since thin subjects get invented details, because inference fills the vacuum. Whether the available sources agree, because contradictory material produces hedged, unhelpful, sometimes alarming answers. And whether you are unambiguously distinguishable from everyone else with your name, which is the single largest cause of unfair machine descriptions we encounter.
The work is therefore less exotic than the label suggests. It is establishing an accurate, consistent, well-structured body of source material, then verifying repeatedly, across systems, that the machines are reading it correctly.
When it applies
The situations this is the right tool for.
You are raising capital or being acquired
Diligence increasingly begins with a model query before a single document is requested.
You are a named executive at a visible company
Your record and the institution's are read together, and the model does not carefully separate them.
You share a name with someone with a record
The most common source of unfair machine descriptions, and the most tractable once the disambiguation work is done.
Your field is technical or specialized
Models perform worst where authoritative sources are sparse, which describes most genuine expertise.
You recently came through something difficult
Models compress narrative badly and tend to preserve the acute phase long after the resolution.
You have no idea what they currently say
Which is the position nearly everyone is in, including people who assume they would have heard.
Process
How the work runs.
Baseline audit across systems
A standard question set, multiple phrasings, run across the major assistants and AI search surfaces. Captured verbatim, dated, and reported as a distribution rather than a headline.
Gap and error classification
Separating outright errors from omissions from conflations, because each has a different remedy and a different timeline.
Entity definition
Structured, machine-readable identity: who you are, what you do, what you are not, and specifically which other person you are not.
Source construction and correction
Building the authoritative material that was missing and correcting what was wrong, at the source, where the systems actually read.
Scheduled re-audit
On a defined cadence against the original baseline, with a named threshold for when a change warrants calling you rather than noting it.
Before you engage
Where the limits are.
Nobody controls model output. Not us, not the labs. Any vendor promising a guaranteed AI result is selling a claim that cannot be honored.
Model behavior also changes without notice. An answer verified in March may differ in June because a system was updated, not because anything about you changed. This is exactly why the service is a standing engagement rather than a one-off project.
We do not manufacture source material, seed fabricated profiles, or attempt to poison model inputs. Beyond the ethics, it is detectable, and the consequence lands on the client.
Questions we are asked
- What exactly do I receive?
- A dated baseline document of current answers across systems, a classification of what is wrong or missing, the remediation work itself, and scheduled re-audits measured against that baseline. The reports include the queries where nothing improved.
- How is this different from monitoring?
- Monitoring watches and alerts. This constructs and corrects. Most clients need both, and monitoring is usually the standing layer that continues after the construction work settles.
- Is this worth doing if I am not well known?
- Frequently it matters more. Well-documented people get reasonably accurate machine answers because the sources are rich. Thinly documented people get invented ones, because the system infers to fill the gap.
- Can you show me examples of your results?
- Not with client identities attached, because that is the pledge and it does not have exceptions. We can walk you through method and mechanism in detail, and the Results page sets out what can be checked instead.
- What does this cost?
- The fee is stated in a written proposal before any work begins, scoped to what the assessment finds. How that is arrived at is set out in full on the How We Work page.
Related
Mechanisms that often run alongside this one.
Is this your situation?
One conversation, in confidence, with an honest reading of whether this mechanism is the right one, including when it is not.