Manufacturing · Document AI · 2025
TriCab RFQ parser — unstructured quotes, made structured
A production AI system inside a cable manufacturer that turns unstructured request-for-quote input into structured, quotable data. Client work — shown as workflow and architecture only, no company data.
By the numbers
The problem
Requests for quote arrive as free text — emails, PDFs, notes — every one formatted a little differently. Someone has to read each one and translate it into the specific fields the quoting process needs. It’s slow, repetitive, and easy to get subtly wrong.
The approach
I built an extraction system that reads the unstructured RFQ input and produces structured, validated fields ready for quoting — with a person still approving the result before it becomes a quote.
- Extraction — an LLM-backed pipeline pulls the relevant fields out of messy input.
- Validation — the output is checked against a schema and the business’s real constraints, so bad extractions are caught, not quoted.
- Human-in-the-loop — the system drafts; a person confirms. The point was never to remove the estimator, only the re-typing.
The interesting engineering isn’t “call a model.” It’s making the output trustworthy enough that a business will put it in front of a customer.
The result
The parser runs inside TriCab today, turning input that used to be hand-processed into structured data the quoting workflow can use — with the approval step intact.