How an Auto Transport Firm Cut Quotes to Under a Second

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.

BaseForge analyzed thousands of historical transactions and built an ensemble machine learning system that automated quoting with instant, data-driven pricing. We deployed it behind a customer-facing API, letting customers and partners connect directly and receive instant quotes, unlocking a significant share of the market.

The team started with the client's transaction history, hundreds of thousands of past quotes across regions, and built pricing features around recency, frequency, location and third-party benchmarks. Some routes showed up rarely in the data or not at all, so the team generated synthetic data by applying statistically derived levers to the nearest high-traffic route, which noticeably improved accuracy on outlier quotes. External variables like fuel prices also moved pricing, so they were sourced from public and paid third-party providers and combined with the client's own parameters. An ensemble of machine learning models produced the price once a quote request carried the minimum set of parameters. The models were packaged in a custom API container so customers and partners could connect directly and get a price back instantly. Accuracy was measured against an unseen random test set, and the team waited until the model cleared a threshold it was willing to stand behind before quotes went live.

Firms that benefit from getting data back to their customers instantly and more accurately.