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The retyping problem.
Somewhere in most companies, a person opens a PDF, reads three fields, and types them into another system. Thousands of times a year. It's the least glamorous AI use case and usually the one worth doing first.
What usually brings people here
- Invoices, delivery notes or forms retyped into accounting or ERP.
- Contracts read manually to find dates, values or renewal clauses.
- Applications or claims processed by hand from attachments.
- Years of records nobody can search because they're scans.
What we build
- Invoice, receipt and purchase-order extraction
- Contract and policy review, pulling out clauses, dates and obligations
- Form and application processing, including handwriting where feasible
- Scanned archive processing to make old records searchable
- Validation rules and human review queues for anything uncertain
How we approach it
- 1
Start with your worst documents
Accuracy on clean samples tells you nothing. We test on the crumpled scan.
- 2
Extract, then verify
Every field gets a confidence score, and low-confidence ones go to a person.
- 3
Compare against the current process
People make errors too. The question is whether this is better, not whether it's perfect.
What you get
- Extraction into your existing systems, not another dashboard
- A review queue for anything below the confidence threshold
- Accuracy measured on your documents during the trial, before you commit
- An audit trail of what was extracted and what a human changed