Case study · From the Pluris libraryReceipt no. 2912
What sucked
The biggest operational and growth bottleneck was their speed and accuracy in quoting transports.
A large and growing market segment, expanding as auto buying became more digital, went untapped because capturing it required instant quote generation, which the company lacked.
What they built
BaseForge trained an ensemble machine learning pricing model on hundreds of thousands of historical transactions, filled gaps on rare routes with synthetic data, and served it through a customer-facing API for instant quotes.
What changed
>94% accuracy
In the models prediction of price against an unseen random testing set
~$2.7B segment opened
Instant quoting opened the remote-buyer and online-retailer segment, roughly $2.7B of a $10.5B vehicle-shipping market by BaseForge's estimate
< 1 second quote delivery
Faster data-driven pricing opened new segments
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