Organizations can’t really control what AI engines say about them.
What they can control is their own data.
This is the core idea behind Apparat. AI engines drift; what doesn’t move is what an organization has published about itself, as long as it is properly structured. Apparat builds authoritative data registers, turning dense documents into clear, atomic statements, in the forms machines are trained to read and use.
Nothing is missing. It just can’t be read.
The problem isn’t that the information doesn’t exist. It’s that the web was built by humans for humans: an organization’s facts are scattered across formats, some of which an AI engine can’t read at all. So when a machine is asked about a company, the answer may be drawn from a good source, a stale one, or none at all, and nothing marks the difference. A correct answer and an invented one look exactly alike.
Apparat builds a machine-readable layer over what an organization has already published: its own record, restated as structured, sourced statements designed to be found and read by AI engines. That record is authoritative because it is the organization’s own, on its own domain, and because every statement is bound to the public source it came from.
Readable by machines. Checkable by anyone.
And it is verifiable, not by the AI, but by anyone. Each statement is sealed, so that what an organization published, where it came from, and whether it has stayed unchanged can be checked at any time, by anyone, without taking Apparat’s word for it. This is the Data Fidelity Register: an open, verifiable format for an organization’s own facts, readable by machines and checkable by people.
Dense documents, read source by source, become a complete set of precise, individually sourced facts.
“The Group delivered a solid performance in 2024, with full-year revenue of €988.8 million, up 12% year on year, reflecting continued momentum across all regions.”
{ "@type": "PropertyValue", "name": "Annual revenue", "value": "988.8", "unitText": "EUR million", "valueReference": { "name": "period", "value": "2024" }, "url": "…/results-2024", "seal": "8bc8c04caf…" }
The authoritative version exists, and it is reachable.
Every fact that defines an organization, structured and precise, on its own domain, in the form machines read. Not a version assembled from stale pages, the one the organization actually published.
The record is curated, not scraped.
Each fact is drawn from a named source and verified against it. What a source does not support is left out. A register is a considered account of what an organization has published, not a dump of everything it ever wrote.
Every fact is bound to where it came from.
For an organization’s own disclosures, the source is the fact of record. A register does not claim more than that. It makes the published record precise and sourced, impossible to confuse with an approximation.
Anyone can check it, without asking.
Each fact is sealed, so that what was published, where it came from, and whether it has stayed unchanged can be confirmed by anyone, at any time, against a public log.
Putting a register in place is light, and most of the work is Apparat’s. A single DNS record points a subdomain at the register, and from then on it is served under the organization’s own name, as static files it controls.
Nothing is installed in the organization’s systems, nothing has to keep running, and there is no dependency on Apparat. A register is files, not software.
The major AI crawlers, operated by OpenAI, Anthropic, Google and Microsoft, already fetch these registers, and the volume is growing.
Among them are the agents that read pages live to answer a user’s question, not only the crawlers that gather data in the background. The register is reaching the systems it was built for.
What we observe is that when an organization’s facts are structured and sourced this way, engines answer questions about it more accurately. Apparat does not, and cannot, control what an AI says, no one can. What it can do is make the authoritative version exist, reachable and machine-readable, so the right answer is there to be found.
We measure the difference with a controlled benchmark, and we run it on an organization’s own facts.
Apparat builds and operates Data Fidelity Registers for organizations. If getting your public facts right matters, the place to start is a short conversation about what a register would cover.
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