Grounded AI for industrial and offshore operations

Your documentation goes back decades. Drawings, specifications, regulations, incident reports, and maintenance manuals, a good deal of it written by people who have since retired. When an engineer asks a question, the answer is usually in there somewhere, and finding it takes hours nobody has.

That is the part most AI tools offer to solve, and it is also where they quietly fail.

Why does ordinary AI retrieval fail on industrial documentation?

Ordinary retrieval splits documents into fixed-size chunks and returns the handful that look most like the question. On a support article that works well enough. On a technical document it drops the thing that changes the answer.

A specification states that a component is rated for a given load, and two paragraphs later states the exception that applies under a particular condition. Those two statements are one fact. Chunking separates them, retrieval returns the first, and the model answers with confidence. The condition never reaches the engineer who asked, and nothing in the answer indicates that anything is missing. That last part has a name: silent omission. The output looks identical whether a passage was read and set aside or never opened at all.

For a chat assistant that is an annoyance. For a lifting plan or an isolation certificate it is a different category of problem.

What does grounding change for an operator?

The grounding service keeps the structure of your documents intact rather than shredding them into disconnected passages, so a clause and its exception stay attached to each other. When your AI asks a question, the service returns the complete relevant context with every claim traced to the page it came from, and a coverage report that states what was included and what was not.

That last part is the one engineers tend to care about most. You can see whether the answer was built on enough of the document set, which is something ordinary retrieval cannot tell you. And when the corpus genuinely does not contain the answer, the service says so instead of assembling a plausible one.

It sits underneath whatever AI tools you already use, as a galvanically separated instance on your preferred cloud. You keep your stack, and we make what it stands on reliable.

What has this actually been shown to do?

The grounding service has read a real engineering training document covering pump performance curves, ISO tolerances, and the duty point, and explained it correctly, including flagging what the document did not state.

A pilot on your corpus is how both of us learn whether it holds on yours.

Where does a first project start?

Usually with a teardown, which is short and narrow by design: you give us a slice of your real corpus and the questions your current tools answer badly, and we show you side by side where ordinary retrieval drops context. If that shows something worth pursuing, a scoped pilot follows with success criteria written in your metrics before any work begins.

The full process is on the pilot page, including what we need from you and what you get at each step.

If your documentation looks like this and you want to know whether grounding helps, tell us what you are working with and we will give you a straight read, including when the answer is no.