Turning vague tender lists into accurate, automatic quotes in seconds instead of hours.
A wholesale distribution company received customer tender lists — vague, generic descriptions of what was needed. No product IDs, no exact product names, just everyday language like “heavy duty black bags” or “blue roll towels”. These lists went out to multiple sellers for competitive quotes.
To respond, staff had to manually search the inventory system for every item on the list, one by one, trying to match vague descriptions to actual products. The system had no fuzzy search, so anything that didn’t match exactly was invisible. They then had to assemble the quote by hand.
We built a matching engine that sits between the incoming tender list and the inventory system, with an automatic quote builder on the other side.
Every inventory item is embedded as a vector, so the system understands what products are — not just what they’re called.
Vague tender descriptions are matched to real inventory items using vector similarity and fuzzy string matching — handling abbreviations, colloquial names, and typos.
Staff review the matched list and approve it. High-confidence matches are pre-selected; ambiguous items are flagged for a quick decision rather than a full manual search.
Once the matched list is approved, the system builds the full quote automatically — no manual assembly, no copy-paste, no missed lines.
Manual inventory lookup and quote assembly replaced by review-and-approve workflow
Entire tender list matched against inventory in seconds, quote built automatically on approval
Semantic matching finds products that exact-match search missed, eliminating errors in final orders
An 8-year-old spreadsheet process replaced with a full platform handling scheduling, portals, dashboards, and event-day ops.
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