The AI Freight Broker
Transforming logistics from manual "Dial-and-Smile" to autonomous orchestration. We built an intelligent system that ingests demand, matches carriers, and negotiates rates without human intervention.
Extracted 12 loads from "Daily_Capacity.pdf"
The Logistics Bottleneck
Freight brokerage is historically low-tech. Our client was scaling, but their manual processes were acting as a parking brake on growth.
Manual Entry Hell
Brokers parsed 100s of unstructured emails daily. 40% of data was entered with errors, leading to wrong pickups.
Slow Quoting
Calculating a quote took 20-30 minutes of cross-referencing lane history. By then, the carrier was gone.
Compliance Risk
Manual vetting of carrier insurance was inconsistent, exposing the brokerage to massive liability claims.
From Inbox to Invoice
An end-to-end autonomous pipeline using Multi-Tenancy Architecture.
Ingest & Normalize
NLP models listen to Emails, APIs, and Web Forms. Unstructured text is converted to JSON load objects.
Smart Pricing
The engine pulls fuel indices and lane history to generate a "Buy Rate" and "Sell Rate" instantly.
Carrier Matching
Algorithms rank carriers by proximity, equipment type, and past performance score.
Communicate
The AI Agent negotiates via email/chat. If confidence is low, HITL (Human-in-the-Loop) is triggered.
The Pricing Algorithm
We didn't just automate data entry; we automated intelligence. The system calculates a Dynamic Volatility Score for every lane.
It handles multi-tenancy, meaning each client gets their own isolated environment with custom rules (e.g., "Never use carriers with < 98% safety rating").
# 2. Adjust for Real-time Fuel Index fuel_surcharge = self.api.get_doe_fuel() * 0.4
# 3. Apply Demand Multiplier (Seasonality) demand_factor = self.predict_demand(origin)
return (base_rate + fuel_surcharge) * demand_factor
Operational Impact
Metrics collected over Q3 2024 Pilot Deployment