Ask an AI who you are. Someone already has.

01

AI Reputation Management

What generative systems say about you when you are out of the room.

The premise of this service is simple. A meaningful share of the people forming a view about you this year will ask a machine instead of reading 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.

Three things determine the quality of a machine answer about you. Whether authoritative material exists at all, since sparsely documented subjects get invented details. Whether the available sources agree, because contradictory material produces hedged, sometimes alarming answers. And whether you are unambiguously distinguishable from everyone else with your name, which is in our experience among the most common causes of unfair machine descriptions.

02

Earned Authority

Published work under your own name, in outlets that exercise editorial judgment.

Authority in a search index and in a language model is largely a function of what you have published and where. Not what you have said about yourself on your own site, because that is a claim, but what an independent outlet decided was worth publishing under your name.

That distinction is doing a lot of work. Paid placements are generally required to be marked and qualified, and they carry different weight from editorial acceptance. An editor's decision to publish is the signal, and it is earned.

This service begins with your standing. We identify the subjects on which your experience carries authority, then develop them into work an editor will accept on merit. You approve every word before it is submitted anywhere, because the byline is yours permanently.

We clear contributor approval before an article is written, then build the piece for publication. How that is done is set out in full on its own page.

03

Presence Architecture

Making you an entity that systems can resolve without guessing.

Search and AI systems read pages differently from people. They attempt to resolve entities, meaning this person, this company, this role, and then attach information to them. When resolution succeeds, everything published about you accumulates to a single coherent record. Left alone, your work is scattered across several partial identities, or merged with a stranger's.

Most reputational problems we see are, underneath, resolution failures. The same person described with three different job titles across four sources. A company whose legal name, trading name, and brand have yet to be stated together anywhere. Two people with one name and no structural signal distinguishing them. Those are resolution problems, and they are fixed by making the record agree with itself.

The work is deliberately precise: structured data that states plainly who you are, consistent representation across every property you control, explicit connections between your entity and your published work, and explicit separation from everyone who shares your name. The reason we treat it as the foundation is leverage. Publishing into an unresolved entity wastes much of its value; publishing into a well-defined one compounds.

04

Leadership Teams

The individual record and the institution's record are read together.

An investor conducting diligence on a company searches the company, and then searches the people. A journalist writing about an organization profiles whoever leads it. A regulator examining a firm examines the individuals who signed. In every case the institutional record and the personal records are read as one document.

Which is why protecting a company's presence while leaving its executives' presences unmanaged achieves considerably less than it appears to. The weakest individual record becomes the accessible line of inquiry, and it is usually the executive furthest from anyone's attention: the CFO with a namesake, the founder whose earlier venture ended badly, the board member whose profile stopped being accurate years ago.

A program covers the team as a set. Each individual receives the appropriate combination of audit, structural work, and published record. The entity receives its own. And the relationships between them are made explicit, so that the connections a diligence process will draw anyway are drawn from accurate material. Programs also cover what only exists at team scale: a response protocol agreed before it is needed, and transition planning, since arrivals and departures are reputational moments for both sides.

05

Monitoring

Reviewed on a schedule by people rather than by an alert rule.

Automated alerting is the easy part. Anyone can set up a keyword notification; the result is a stream of mentions with no indication of which ones matter. The valuable part of monitoring is judgment, which turns a stream of mentions into decisions.

Our review adds three layers to automated alerting. It is measured against a documented baseline, so a change is identifiable as a change rather than as noise. It is reviewed by someone who knows your situation, so a new mention is assessed for trajectory rather than counted. And it has an agreed escalation threshold, so you know in advance what will produce a call at nine at night and what will appear in the next report.

The AI dimension is now a substantial part of this. Model answers change without any external event: a system is updated, a retrieval index shifts, and the description of you moves. Nothing was published and nothing happened, but the first impression a counterparty receives is different. Detecting that requires deliberately re-asking the questions on a schedule, because nothing will alert you to it. The purpose is time: almost every problem in the reactive practice would have been cheaper if it had been identified in week one rather than month six.