Technical documents arrive from sites in different countries, in different languages, carrying conventions that every local team knows and nobody ever wrote down. The hard part is not the words. It is everything the document assumes you already know.
The documents are not the hard part. They come in from sites in different countries, in different languages, and the identifiers and results inside them are exactly what the business needs. The obstacle is the context around them, and that context is implicit: a number that is a sample identifier at one site and a part identifier at another, a field whose meaning depends entirely on who filled it in. The ask is to read those documents reliably enough that the business can trust the output, in every language they arrive in.
Not translated. Understood. Those are different problems, and only one of them is solved by a bigger model.
This one we have taken further than a conversation. A global chemicals leader had a document estate that generic agentic platforms could not process with the reliability the business required. The models were clever enough to fill in the missing context themselves, and a filled-in gap is where the failures start. So we studied the documents and the people who produce them, built a custom parsing and context layer around what we found, and had a live proof of concept running in one week. It is scaling globally.
Referenced as a global chemicals leader, and no more specifically than that. Paraphrased, like everything on this map.
The missing piece does not live in the file. It lives with the people who produce the file. A general model has nothing to retrieve, so it does what it was built to do: it fills the gap with the most plausible thing and moves on, at speed, without hesitating.
Directional only. We do not put figures on someone else's operation, and these pools are heavy enough without decoration.
A document that leaves its site has to be read by one of the handful of people who know that site's conventions. They are senior, they are busy, and everything behind them waits.
The conventions become part of the system instead of part of someone's memory, so reading a document stops routing through the same few desks.
A record that resolved the wrong way looks exactly like a correct one. It surfaces downstream - in a reconciliation, a complaint, an audit question - and by then it has been acted on.
Ambiguous fields resolve against a defined mapping instead of a plausible guess, so the wrong record stops being written in the first place.
Where the output cannot be trusted, teams quietly stop using it. Whole document estates stay unread rather than risk being read wrong, and their history is unavailable when a question is asked.
Output the business trusts is output the business uses - which is what brings the untouched estate into scope at all.
Two engines run in production today - Analytical Lab Reports and account Knowledge Twins. Everything else on this map is an extension on the same foundation.
This entry is the first of those two. Analytical Lab Reports is a custom parsing and context layer wrapped around a document estate: it works out what a field actually is at a given source, maps the relationships explicitly, and hands over structure rather than text. Multilingual technical documents are that same mechanism pointed somewhere harder - more dialects, more implicit convention, the same rule that nothing is inferred where it can be defined.
The discipline underneath is easy to state and unglamorous to do. Make the implicit explicit, once, with the people who hold it. Then let deterministic systems execute against it, so the same document produces the same record every time it is read.
Read the full use caseIt runs inside your infrastructure, within your boundary. Technical documents carry specifications, methods and identifiers you would never hand to a public endpoint, so they do not leave. Your freedom of action stays intact with them: which models do the reading, where the workload runs, and what it costs you to run it.
Back to the AI ROI MapRun the exercise on our parsed data first. Then put your worst source in front of us - the one where the meaning of a field depends on who filled it in. We would rather be tested than believed.
On-prem. Your data never leaves your boundary.