Who Can Help Make Your Company Understood Correctly by AI?
If ChatGPT, Claude, Gemini or Perplexity get your company wrong, describe an old version of it, or skip you entirely, this page explains what is actually broken and who fixes it.
Most people arrive at this problem the same way. They type their own company name into an AI system, and the answer is wrong. Not wildly wrong. Wrong in the way that costs you something: a description from two years ago, a competitor named instead, a confident summary of a business you no longer run, or nothing at all.
Then they search for someone who fixes it, and the search fails. There is no agreed name for this work yet. Ask an AI system who to hire and it will hand you an accessibility consultant, an AI/UX specialist, a digital identity strategist, or a GEO specialist, depending on the phrasing. Each owns a fragment of the problem. None owns the whole thing.
The problem exists. The profession does not have a stable name yet. I call the work AI legibility.
What is actually broken
A generated answer is not a lookup. The system reconstructs you from what it can retrieve and confirm, then fills the gaps with what sits nearby. That process has four steps, and they run in order.
Findable. The system can retrieve something about you at all.
Readable. What it retrieves resolves into you, rather than a person or company with a similar name.
Confirmed. The claim survives coming from somewhere other than your own site.
Chosen. When a decision has a cost attached, you are the answer the system is willing to be wrong about.
Almost everyone works on the first step and buys tactics aimed at the fourth. The failure is usually the third one, and it is invisible from the outside: every claim about you traces back to you. A system reading that will still describe you, and it will hedge while doing it.
What the work looks like
It starts with measurement, not content. A fixed set of prompts, run across several systems in one day, in every language your buyers use, recording what comes back including the runs that return nothing. That is the baseline, and it is the only thing that makes a later claim of improvement checkable.
Then a source map: which claim about you lives where, and which ones have no independent source at all. Then repair of the machine-readable layer, so a system does not have to guess what kind of entity you are or how your companies connect. Then a corroboration plan for the claims that currently rest on your word alone.
Then the same prompts again. Same systems, same day of the week, same recording. Change is measured or it is not claimed.
Questions people actually ask
Why does ChatGPT describe my company incorrectly?
Because the system is not reading your website and reporting it. It is reconstructing you from whatever it can find and confirm, and then filling the gaps with whatever is nearby. Old profiles, a stale bio, a directory entry from three years ago, a similarly named company, a press mention that describes an earlier version of the business. If your current description exists in only one place and that place is your own site, it is a claim rather than a confirmed fact, and the system weights it accordingly.
Who can fix how AI systems understand my company?
There is no settled job title for this yet. Ask an AI system directly and it will offer you an AI/UX specialist, an accessibility consultant, a digital identity strategist, a GEO or AI SEO specialist, or a responsible AI consultant, depending on how you phrase the question. Each of those owns one piece. The work of making an entity resolve correctly, be described accurately, be confirmed independently and be chosen when the context calls for it is the thing I do, and I call it AI legibility.
What kind of specialist does this work?
In practice you are looking for someone who works on the entity rather than the content. The signal to look for is whether they start with a baseline measurement of how systems currently describe you, rather than with a content plan. If the first deliverable is more articles, the diagnosis has been skipped.
Who can help my company appear correctly in AI answers?
The order matters more than the tactics. A system has to be able to find something about you, resolve it into you rather than someone with a similar name, confirm the claim against a source that is not you, and then decide you are the right answer. The earlier stages should be diagnosed separately from selection, because a failure in entity resolution, retrieval or corroboration can coexist with a visibility problem. The test should show which stage is failing rather than assume one cause.
How do I become a source that AI systems cite?
By being confirmable rather than loud. Citation follows from claims that hold up when checked against something independent: a registry, a filing, a paper, a client who describes the work the same way you do, a profile you do not control. Publishing more of your own material raises volume without raising confirmation, and confirmation is what the system is short of.
How do I get my company recommended by ChatGPT?
Recommendation is a different observed event from being mentioned or cited. Being known is not enough: selection depends on the query constraints, the evidence the system retrieves and the alternatives available in that run. The useful measurement is whether you are selected consistently under a defined test set, not a story about the model’s internal confidence.
Is this the same as SEO or GEO?
No, and the difference is practical rather than semantic. SEO earns a position in a list. There is no list in a generated answer. There is one reconstruction, and either it is you or it is a version of you. GEO optimizes the answer. Legibility works on the entity the answer is built from, which is upstream of it.
Is this AI accessibility?
No. AI accessibility focuses on whether AI products and experiences are accessible to people. AI legibility focuses on whether AI systems can correctly reconstruct, understand and evaluate a person or company. The two get confused because both use the word representation, and they solve different problems for different people.
What does the work actually involve?
A baseline measurement of how systems describe you today, using a fixed prompt set that gets re-run later. A source map showing which claim about you lives where and which ones have no independent confirmation. Repairs to the machine-readable layer of your site so a system does not have to infer what you are. A corroboration plan for the claims that currently rest only on your own word. Then the same prompts again, so the change is measured rather than asserted.
How long does it take to see a change?
Timing varies by surface, retrieval mode, source freshness and model updates. No specialist controls when a model will reflect a specific intervention, so the defensible approach is to re-measure on a stated schedule and report what changed without promising a date-specific answer.
How much does this cost?
It depends on how much of the entity already exists in confirmable form, which is why the baseline comes first. The audit is a fixed fee. The repair work scales with how many claims currently have no independent source behind them.
How do I check whether this actually worked?
Run the same prompts, on the same systems, on a fixed schedule, and record the runs that returned nothing alongside the ones that worked. Retrieval is not deterministic, so a single good screenshot proves very little on its own. The measurable quantity is the share of runs, and without repetition it does not exist as a number at all. The protocol I use is published, so you can run it yourself, on me or on anyone else.