AI document extraction with your rules encoded.
We encode the extraction rules per document type — which version wins, which field maps where — so documents are read on arrival, not re-keyed.
Official services partner of the platforms defining AI
NVIDIA
Anthropic
The documents never stop. Someone re-keys them.
From operations teams we have sat with: ops managers, controllers, revenue-cycle and practice-ops leads at mid-market and PE-backed companies between $20M and $500M — not shopping for software, watching the document pile eat a person’s week.
The weekly item-change PDF, parsed store by store
A 7–10 page item-change PDF lands from the co-op every week, and someone breaks it down store by store — stale location or on-hand data breaks it.
The 20–50 tab monthly mega report
The co-op pushes a 20–50 tab Excel workbook each month; an ops person pulls the count sheets out and pushes them to the stores as PDFs.
Every initialed box on every scanned consent form, checked by eye
Field staff scan consent forms in and revenue-cycle clerks verify every initialed box one by one — the kick-back rate to the field stays high.
Timesheets hand-delivered by Dropbox every cycle
Timesheets and payroll reports arrive by Dropbox for hand-cleaning every cycle — when the vendor changed, the layouts changed and the routine broke.
Seventy documents per case, assembled by hand
Verifying one eligibility case means pulling 70 documents reaching 5 years back across 18 third-party services — per case, every time.
Re-keying is a payroll line, not a nuisance.
What the manual way looks like when every document is handled by a person: it arrives, sits in an inbox, gets read, gets re-keyed, and the errors surface weeks later. Your numbers will differ — the first document type we shadow puts figures on yours before anything gets built.
From anonymized engagements — a ~20-store retail co-op member, multi-branch home health, a physician-group practice
AI document search — every field findable again after extraction.
No document-management migration, no new system to learn. The rules your best re-keyer applies become the extraction, and every extracted field keeps a pointer back to its source page.
- 01
Shadow one document type
We watch one document type for a week — every layout, every exception, every place the data lands — before anything gets encoded.
- 02
Encode the extraction rules
Which version wins, which field maps where, the confidence threshold on each — documented per document type, auditable, yours to keep.
- 03
Humans see exceptions only
The AI reads every incoming document; the exceptions the rules cannot resolve queue for a person, and approved fields write through the same day.
The tool reads the document. It doesn’t know your rules.
Every tool on page one will read the document. None of them knows the addendum supersedes the contract, that this vendor’s “each” is a case, or where the field goes in your ERP.
Our AI does the reading; your rules — encoded, auditable, yours — do the judging. Approved fields write through with the source page attached.
We were burned before — the bar was proof on our own documents, field by field, before anyone relied on it.
Led by Peter Enestrom.
Asked by ops managers and controllers.
The straight answers, before you book anything.
AI document extraction is the process of pulling defined fields out of recurring business documents — PDFs, scans, and email attachments — and structuring them so another system, like an ERP, can use them. Done properly, each extracted field keeps a reference back to the exact source page it came from, so any value can be checked.
OCR turns an image of a document into text; it does not know which document it is reading, which version wins, or where a field belongs in your system. Extraction adds those rules: the document type is recognized, fields are mapped against encoded definitions, and low-confidence values route to a person instead of writing through. OCR is one ingredient; the rules are the system.
Any recurring document that arrives as a PDF, scan, or email attachment: price sheets, item-change notices, consent forms, terminal reports, timesheet exports. Scanned and initialed fields are handled with confidence thresholds — values the rules cannot resolve go to a human queue rather than being guessed at.
Approved fields write into your system of record through your existing import or posting process, with the source-page reference attached to each field. Nothing writes silently: the rules set confidence thresholds per field, and a person reviews only the exceptions the rules cannot resolve.
Layout drift is a named failure mode, not a surprise. When a vendor changes the form, the encoded rules flag the drift — fields stop matching their expected patterns and route to the exception queue — instead of silently re-keying garbage. The rule set is then updated against the new layout.
Start with one workflow.
Tell us where your team loses hours. We will come back with a straight answer on whether AI can help, what it would take, and what it would pay.