Asking a procurement database in plain language, and getting the evidence back

Executive summary

A heavy manufacturing group's buyers needed answers that lived in two incompatible places: a transactional procurement database and a large pile of internal documents. We built a conversational assistant on a multi-agent orchestration layer. One specialist translates a question into a validated read-only query, another retrieves from a hybrid document index, and a top-level agent decides which internal sources a given question actually needs. Every answer carries its evidence, whether that is the executed query and the rows it returned or the document page a passage came from. The same backend serves two front ends: a full internal console with analytics and knowledge-base management, and a chat-only surface for staff who simply want to ask.

Business context

The client is a heavy manufacturing group whose margin is set in large part by what it pays for raw material inputs. Buying decisions depend on facts spread across two systems that do not talk to each other. The first is a transactional database holding purchase records, supplier offers and inbound receipts, accumulated over years and named accordingly. The second is an unstructured library of specifications, internal procedures and vendor correspondence. Answering a question of the form "what did this input cost us, from whom, and does that match what the vendor committed to" meant querying the first and reading the second, and almost nobody in the organisation could do both. That gap, rather than any shortage of data, is what the engagement set out to close. It also fixed the hard constraint on the design: for a procurement team, an assistant that produces a confident number nobody can trace is worse than no assistant at all, because a number that looks authoritative is a number that gets used.

The challenge