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How it works
Why general-purpose models struggle with industry paperwork, and how we build and deploy private models that do not.
A drawing's scale sits in a title block, a door schedule is a table that refers to marks on another sheet, and a bill of lading packs parties, quantities and codes into fixed boxes. A model that reads documents as a stream of text loses most of that structure.
CSI section numbers, sheet numbers, HS codes, incoterms, MSA rate schedules. A general-purpose model has seen these, but it has not been trained to treat them as the identifiers they are, so it confuses one for another.
The costly mistakes are where two documents disagree: a specification that contradicts a drawing, an invoice that does not match the contract rate, a quote that leaves out a scope item. Finding them means reading the documents together, not one at a time.
A chatbot will answer anyway, fluently. In a bid, a customs entry or a payment run, an answer you cannot trace back to the page it came from is a liability, not a time saver.
Your documents
Tenders, contracts, drawings, records
Private environment
Your private model
Trained on your documents
Programs and apps
What your team uses
Hosted by us, or in your own tenant by agreement
Third-party AI vendors
Nothing is sent to them
We start with one job: who does it, which documents they use and what a good result looks like. We agree what done means and how it will be measured before anything is built.
We start from an open-weight model and fine-tune it on documents from your domain, so it learns your formats and your vocabulary. Nothing is sent to a commercial AI API to do this.
Around the model we build the logic that knows your rules, the checks that tie every answer to its source, and the program or application your team will actually use.
The system runs in our hosted environment or, by agreement, in your own cloud tenant or on your own hardware. Either way, no third-party AI vendor sits in the path.
We operate the system, update it as codes, rates and formats change, and measure it against real results. Your documents are never used to train anything for anyone else, and can be exported or deleted on request.
A Prototype Sprint tests the approach against your real data in two to three weeks, before you commit to anything larger.