Messy documents in. Structured, checked data out.
Intelligent document processing reads unstructured files — scans, PDFs, phone photos, email attachments — extracts the fields that matter, maps them into your systems or templates, and routes only the exceptions to a person. The measure of a good system is not how much it reads. It is how few documents a human ever has to open.
- This is the fastest category to prove. One export business went from kickoff to production in 60 hours.
- Accuracy is a number you set, not a promise. One deployment moved error rate from around 10% to under 1%.
- The exception path is the product. Anything the system isn't sure about goes to a person with the uncertain field highlighted.
Six document piles that quietly cost a headcount
If a team is retyping the same fields off a PDF every week, the arithmetic is usually already decided.
Invoice and remittance intake
Line items, tax, PO references and payment terms pulled from every supplier's format, matched against the purchase order, with mismatches queued rather than guessed.
Paperwork standardization
Certificates, specifications and packing documents arrive in a different shape from every manufacturer, some as blurry scans. They leave as one branded, consistent template. Running in production today.
First-notice-of-loss intake
Claim forms, photos and adjuster notes structured into the claim record, with the fields that drive routing extracted first so the file lands in the right queue on day one.
Clause extraction across an archive
Renewal dates, liability caps, assignment and change-of-control clauses pulled from executed agreements into a table you can actually filter, with the source paragraph attached to every row.
Referral and fax intake
Faxed referrals and clinical attachments read, patient and payer details matched to the record, missing documentation flagged before submission rather than after a denial.
Bills of lading and customs packs
Consignment details reconciled across the bill of lading, commercial invoice and packing list, with discrepancies surfaced before the shipment is held rather than after.
Five mechanisms, in this order
- Extraction that survives bad scansOCR tuned to preserve tables, forms and structure from low-quality images and phone photos, because that is what actually arrives.
- Field mapping with ambiguity resolvedExtracted content is mapped into your schema or template, and genuinely ambiguous fields are marked as ambiguous rather than filled with a confident guess.
- Confidence thresholds you setYou choose the accuracy bar per field. Anything below it routes to review. Getting this dial right is most of the value.
- A review step built for speedA side-by-side editor showing the source and the extraction together, so a person confirms in seconds instead of re-reading the document.
- Export that matches your house formatLetterhead, headers, footers and downstream system fields applied on export, so nothing is reformatted by hand afterwards.
Kickoff to production in 60 hours
Client names and locations are withheld; figures are drawn from delivery records and approved business cases.
Document standardization, shipped in 60 hours
A chemicals export business connecting overseas buyers with domestic manufacturers. Every manufacturer sent paperwork in a different format, some as blurry scans or phone photos. The team spent hours a week reformatting it by hand.
- OCR extraction that preserves tables, forms and structure even from low-quality scans
- A standardization layer that maps extracted content into the company's own templates, resolving ambiguous fields
- A side-by-side editor for quick human validation before anything ships
- Automatic branding: letterhead, headers and footers applied on export
Delivered by a Proof Pod
Read the full case →Three numbers that decide whether to build
- Volume. How many documents a week, and is it growing with revenue?
- Handling time. Minutes per document today, including the rework when a field is wrong.
- Cost of an error. A wrong figure on an internal report and a wrong figure on a customs form are not the same project.
Bring those three to the scoping call and we can tell you in the same conversation whether this is worth building.
Three piles we will tell you to leave alone
- Low volume, high stakes. Forty documents a year that each carry legal exposure deserve a careful human, not a pipeline.
- Documents nobody agrees how to read. If two experienced staff extract different values from the same form, the definition is the problem and no model fixes it.
- Already-structured data. If the supplier can send a file or expose an API, take the integration. It will be cheaper and more accurate than reading their PDF forever.
FAQ
What to ask before you automate a document pile
What is intelligent document processing?
A system that reads unstructured files, extracts the fields that matter, maps them into your systems or templates, and routes only the uncertain cases to a person. It differs from classic OCR in that it handles variation in layout and wording rather than requiring every document to arrive in the same shape.
How accurate is it, really?
Accuracy is a threshold you set per field, not a single headline number. Anything below the bar routes to human review with the uncertain field highlighted. On one production system the error rate moved from around 10% to under 1%, measured against the client's own baseline.
Our documents are scans and phone photos. Does that break it?
No, and it is the normal case. Extraction is tuned on your worst inputs rather than clean samples, because clean samples are not what your suppliers send. Quality that falls below a usable threshold is rejected at intake with a reason, not silently misread.
Where does the data go?
Into your own cloud account, region-bound where it matters. PII and PHI are redacted before any model call where your policy requires it, and every extraction is traceable to the source page for audit. Your data is never used to train a model.
Most AI pilots die in the demo. Ours don't.
We define one measurable metric before we write a line of code. If it hasn't moved inside the impact window, the pod keeps building until it does, at no additional fee. You never pay for AI that just sits there.
Scope a Proof Pod →One workflow. Real data. A number that moves.
Bring a week of real documents, including the bad scans. That is the fastest scoping call we run.
Scope a Proof Pod →