Project 01 · AI integration in process
AI inside a document-heavy business process.
An intake system that reads incoming documents, checks them against business rules and sends only the exceptions to people.
Intake
Documents arrive by email, upload or scan and are queued.
- Python
- FastAPI
- Language model with structured output
- PostgreSQL + pgvector
- Temporal workflows
- Next.js review interface
The challenge
An operations team receives hundreds of supplier invoices, delivery notes and claim forms every day, as PDFs and email attachments. Staff re-type the details into the company system and check each one against purchase orders by hand.
The work is slow, errors slip through, and the most experienced people spend their day on the simplest cases. Intake Engine takes over the routine reading and checking, while people stay in charge of every decision that matters.
What the system does
- 01
Reads each document and extracts the key fields into a strict, predefined structure.
- 02
Checks every field against business rules and existing records, such as matching an invoice to its purchase order.
- 03
Approves clear cases automatically and sends the rest to a review queue, with the fields pre-filled and the evidence highlighted.
- 04
Writes approved results back to the system of record and keeps a complete audit trail of what was read, checked and decided.
How it works
From input to decision in five steps.
- 01
Intake
Documents arrive by email, upload or scan and are queued.
- 02
Extract
A language model reads the document into a fixed schema, with a confidence score for every field.
- 03
Validate
Deterministic rules check totals, dates and references against existing records.
- 04
Route
Clear cases go straight through. Anything uncertain goes to a person.
- 05
Record
Results are written to the business system with a full audit log.
What this shows
- Using language models safely inside a process: strict schemas, plus a rules layer that catches mistakes.
- Human-in-the-loop design, where people handle exceptions rather than volume.
- Measuring accuracy on a test set before anything goes live, and monitoring cost and latency afterwards.
Where it applies
- Finance and accounts payable
- Insurance claims
- Logistics documentation
- Public administration
Contact
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