A top-50 U.S. freight broker was losing revenue to manual load matching, high empty-mile rates, and pricing decisions made on gut instinct. RTS Labs deployed an AI-powered freight matching engine in 10 weeks — driving 18% revenue growth, cutting empty miles by 31%, and reducing match time from 47 minutes to under 4.
National Logistics Leader
Transportation & Logistics
AI-Powered Freight Matching & Load Optimization
Python
XGBoost
AWS SageMaker
Apache Kafka
Our engineers will map your workflow and define a ship date in a 2-week Discovery Sprint.
The client, a top-50 U.S. freight broker by load volume, was growing fast — and their operations were starting to buckle under the weight of that growth. With over 1,200 active carrier relationships and thousands of load requests moving through their system each week, dispatchers were still making matching decisions the same way they had at 200 loads per day: spreadsheets, phone calls, and institutional knowledge.
The cracks were measurable. Empty miles — trucks completing a delivery with no return load — were running at 34%, well above the 22% industry benchmark. Match confirmation was taking an average of 47 minutes, meaning time-sensitive freight was routinely being offered to competitors who could respond faster. And because matching quality depended on which dispatcher handled the load, pricing and carrier selection varied significantly across the team — leaving both margin and customer experience on the table.
Trucks were completing 34% of miles without a paying load — well above the standard 22% industry benchmark — directly compressing the net profit margin on every route the company operated.
Dispatchers averaged an excessive 47 minutes to confirm a load-to-carrier match. Time-sensitive shipments were going to competitors who could respond in minutes, not nearly an hour.
Lane pricing was set by dispatcher intuition instead of real-time market signals. On high-demand corridors, the company was systematically underpricing — surrendering margin on valuable freight.
RTS Labs approached freight matching as a real-time recommendation problem, not a rules engine. Rather than replacing dispatchers, the system was designed to give them the right answer in seconds — surfacing ranked carrier recommendations with confidence scores and automated pricing guidance, and preserving human judgment for edge cases. The architecture centered on a gradient boosting model trained on 18 months of historical load and carrier data, served via a low-latency API integrated directly into the client’s existing TMS.
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