AI Logistics Solutions

The Challenge

FTL brokers were quoting from gut feel and last week's rates, leaving margin on the table — or losing the load.

Manual quoting also slowed sales when minutes decided who got the shipment.

Our Solution

We built an AI engine that ingests real-time market signals, considers route specifics and returns a defensible quote in seconds.

Brokers get a live number to negotiate with instead of a guess.

What We Built

• Real-time market signal ingestion and analysis.
• Route optimization for accurate cost modeling.
• Instant FTL quote generation with confidence indicators.
• ML-driven pricing that adapts as market conditions shift.

The Approach — What We Weighed

The core question was how to price freight defensibly — a broker won't trust a number they can't explain to ops or to a customer, so the pricing layer had to be more than a black box.

Options on the table

• A rules-and-lookup engine over historical lane rates — transparent and cheap, but it drifts the moment the market moves and can't read live demand.
• A managed third-party rate API as the single source of price — quick to integrate, but it's a rented number the broker can't tune and pays for per quote.
• An in-house ML model fed by real-time market signals and route specifics — more to build, but the pricing logic and the data are the client's asset.

What we proposed

We built the ML engine in-house, with market-signal ingestion the client owns and confidence indicators surfaced on every quote.

The reasoning was margin discipline and trust — a model that adapts to live conditions protects margin, and an explainable, defensible number is one a broker can actually negotiate against.

Results & Value Delivered

Quotes in seconds

Sales cycles compressed; more loads won.

Better margin discipline

Pricing reflects live market, not last week's gut call.

Defensible numbers

Quotes are explainable to ops and to customers.

Capabilities Used