Document Intelligence Pipeline
Contracts, statements and forms turned into structured data you can query, with a confidence score on every field and a human in the loop wherever it is low.
- To first release
- 2 weeks
- Confidence scoring
- Per field
- Claude
- Python
- PostgreSQL
- Temporal
- S3
The problem
Document work is expensive precisely because it is boring. A team reads the same shapes of contract, invoice or claim form, types fields into another system, and the mistakes are invisible until something downstream reconciles badly. Off-the-shelf extraction tools claim high accuracy on their benchmark and then meet your actual documents, which are scanned, annotated, and nothing like the benchmark.
How we would sequence it
- 01Start from a sample of your real documents, including the bad scans and the edge cases
- 02Extract to a defined schema rather than to free text, so the output is usable without a second pass
- 03Score confidence per field, and route anything below the threshold to a person
- 04Measure against a hand-labelled set so accuracy is a number rather than an impression
- 05Feed corrections back, so the reviewed cases improve the next run
This solution describes our method for a class of problem. It is not a record of a completed client engagement, and the figures above are scope estimates rather than measured results.
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