Wholesale distribution, 1.5M+ records, 20,000 items
Every growing distributor drowns in its own product data. The same item gets entered a dozen different ways until the catalogue is a mess of free-text names no system can trust. Cleaning it by hand means weeks of analyst time.
We built an AI matching engine that reads each raw record, finds its true match in the master catalogue, and scores its own confidence. People only touch the genuinely ambiguous cases.
The engine resolves 90 to 95% of records on its own.
Measured
Out of about 20,000 items, only about 500 ever need a human eye.
Measured
Weeks of manual mapping became a same-day job, and the system gets sharper with each correction.
Measured
Fuzzy text matching plus AI semantic embeddings plus a confidence-scoring layer, with human-in-the-loop review and a learning feedback loop.
Next step
Book a discovery call. First quote in 3 hours.
Construction
