# The AI industry has the same visibility problem it sells the cure for

There is a particular irony in AI companies being invisible to AI. Ask an assistant for "a vector
database that runs on-premise" or "an OCR API that handles handwritten forms" and the shortlist is
short, repetitive, and usually a few years out of date — because the answer comes from documentation
and forum threads that were text, not from the launch sites that were animation.

## Documentation is your best crawler surface, and you already have it

If your docs are plain HTML or markdown, a crawler reads them properly. This is why some small tools
punch far above their marketing weight in AI answers: their reference pages state precisely what the
thing does, in what language, with what limits.

The gap is that documentation answers *how*, not *whether*. Nothing on a docs page says "use this
when you need X and not Y" — and that is the sentence a model needs to recommend you to somebody
describing a problem.

## Write the positioning a model can quote

- **What class of thing is it.** Vector database, OCR API, feature store — the ordinary category word.
- **What is the boundary.** Runs on-premise, no GPU required, handles up to N documents a second.
- **What it is not for.** Naming the wrong use case makes the right one unmistakable.
- **Pricing shape.** Open source with a paid tier, usage-based, seat-based. Say which.

## What is listed here

Each company below has a listing page and a markdown twin, linked from this article, the category
feed and llms.txt, with an open count of how many times each named AI crawler fetched it — from
server logs, unedited.

## Companies listed in this category

_No companies listed here yet._

Directory: https://pixelsforbots.com/c/ai · All categories: https://pixelsforbots.com/llms.txt