Proactive practice

Ask an AI who you are. Someone already has.

An investor, a journalist, a counterparty's analyst, a search committee. They asked a machine, and the machine answered with total confidence from whatever it happened to find. This practice is about what it finds.

What changed

The question stopped being what ranks, and became what is understood.

For twenty years, being findable meant occupying positions on a page of links. A person searching your name saw ten results, formed their own impression, and clicked. The work was to influence which ten.

That is no longer how most first impressions are formed. A generative system is asked a question and returns a paragraph. It does not present ten options; it presents a conclusion, in confident prose, with the sources it used compressed out of view. If the sources were thin, inconsistent, or wrong, the paragraph is wrong, and it does not sound wrong.

Which means the objective has changed. It is no longer enough to rank. You have to be legible: an entity these systems can resolve without guessing, described consistently across sources that agree with one another. That is a construction problem, and it takes time.

Try this now

Open any AI assistant and ask it three questions.

  1. "Who is [your name]?"
  2. "What is [your company] known for?"
  3. "Are there any concerns about [your name]?"

Note what it gets right, what it invents, and what it declines to answer. That is the material a counterparty is reading. The third question is usually the instructive one.

The Machine Record

What the machines are saying this month.

Our correspondence on how search and AI systems are describing people and companies: what changed, what it means, and what we are watching. Sent when there is something worth sending.

The Machine Record, sent when there is something worth sending. No tracking pixels, and one click unsubscribes.

Questions we are asked

Why does what an AI system says about me matter?
Because it is increasingly the first and often only answer someone reads. A person conducting diligence used to scan a page of links and form their own view; now they frequently ask a model and accept its summary. That summary is assembled from sources, and the sources can be wrong.
Can you control what an AI model says?
What we change is what it retrieves and what those sources say, which is where the answer actually comes from: we establish that accurate, well-structured, authoritative material about you exists, and we get inaccurate material corrected at its origin. Nobody edits a model's weights, including the labs' own customers. Working on the sources is slower than editing a page and considerably more durable.
Is this the same as SEO?
It overlaps and it is not the same. Search engine optimization aims at ranking a page. This work aims at an entity being understood, so that a system asked who you are can resolve the question consistently, from sources that agree with each other. Ranking is one output of that rather than the goal.
We are not in any trouble. Why would we start now?
Because the material that protects you takes months to accumulate and cannot be assembled during a crisis. The firms that come through a difficult week intact are the ones that already had a substantial, accurate record in place. Building it afterwards looks exactly like what it is.

Start before it is urgent.

A conversation about what search and AI systems currently return about you, and what a durable presence would take. No obligation, and no charge for the reading.