Document Processing Pipeline
Structured extraction from recurring business documents, with confidence thresholds and a human review path for anything uncertain.
- Sector
- Operations / back office
- Discipline
- Automation
- Technology
- Python, OpenAI APIs, FastAPI, PostgreSQL, Docker
The problem
Recurring documents such as invoices, delivery notes and forms arrive in inconsistent layouts and get re-keyed by hand. Naive automation is worse than none: a system that silently extracts a wrong figure is more expensive than a person reading the page.
What we built
We built a pipeline that extracts structured fields from documents, scores its own confidence, and routes anything below threshold to a human review queue. Every extraction stores the source document and the exact region it came from, so a reviewer can verify a value in seconds.
Architecture
How the system fits together.
- 01
Intake
Documents collected from a monitored mailbox or folder, deduplicated and queued for processing.
- 02
Extract
Text and layout extraction, then structured field extraction against a defined schema with typed output.
- 03
Score
Per-field confidence plus rule checks such as arithmetic consistency and required-field presence.
- 04
Route
High-confidence records pass through; anything uncertain enters a review queue with the source region highlighted.
- 05
Deliver
Approved records written to the destination system, with a full audit trail of automated and human decisions.
Outcome
What the system delivers.
Capability delivered, stated plainly. We do not attach business metrics to this work that we are not in a position to verify.
- Typed, schema-validated output instead of free-form text
- An explicit confidence threshold separating automatic from reviewed records
- Source-linked extractions, so any value can be checked against the original page
- A complete audit trail covering both automated and human decisions
- Python
- OpenAI APIs
- FastAPI
- PostgreSQL
- Docker