Every new customer order started the same way: a bill of materials landing in someone's inbox, and one of the company's most senior engineers setting aside other work to turn it into a matched, priced quote by hand, line by line, part by part. It was slow, it didn't scale past a handful of quotes a week, and the knowledge of which parts were equivalent, which suppliers were trusted for which components, and how pricing logic applied in edge cases lived in that one engineer's head.
We connected directly to the customer's existing parts catalog, supplier data, and pricing rules, no separate database, no rebuilding their parts taxonomy from scratch. Every incoming BOM is parsed into a structured product tree the moment it arrives, matched line-by-line against the catalog using the customer's own equivalency logic, and anything new or ambiguous is flagged rather than guessed at.
We scoped this deliberately narrow at first: one product line, one class of incoming BOM, backtested against the company's own historical quotes before the match logic was trusted with a live one.
The senior engineer who used to own every quote by hand now reviews exceptions instead of starting from a blank BOM every time, and every match the system makes is traceable back to the exact part record and rule that produced it.
Industry: Electronics (Contract Manufacturing)
Cloud Platform: AWS
Use Case: Quote & BOM Matching
This is exactly the shape of workflow we scope as a first pilot - one segment, one clear bottleneck, backtested against your own historical data before it's trusted with a live recommendation.
Get in touch