AI Freight Broker

Freight brokerage has long relied on manual processes—large teams sifting through emails, messages, and calls to match shipping demand with carrier availability. This inefficiency slows down operations, increases costs, and limits scalability.

To transform this process, we developed the AI Freight Broker—an intelligent system that automates freight matching and communications across platforms.

We first built NLP pipelines that interpret natural-language freight requests (from emails, chats, or forms) and convert them into structured load data—pickup, drop location, cargo type, weight, etc.

To ensure high accuracy, we integrated a Human-in-the-Loop (HITL) system for resolving ambiguities and validating edge cases. The AI agent was trained to mimic human browsing and access freight APIs or portals for real-time data pulling—removing the need for manual entry.

We also conducted domain-specific research to create custom pricing models based on geography, cargo type, fuel cost, and lane history, ensuring competitive and dynamic quoting.

To support multiple clients or logistics partners, we enabled multi-tenancy, allowing each user to operate within their customized rules, contracts, and workflows.

The AI Freight Broker can:

• Ingest freight demand and supply data from emails, CRMs, and web forms

• Match loads to carriers automatically using smart logic

• Autonomously communicate with brokers, carriers, and clients via email/chat

• Generate quotes and updates based on live pricing models

By automating freight matching and eliminating back-and-forth delays, the AI Freight Broker significantly boosts broker productivity, lowers overhead, and reduces lead-to-booking time.

The system ingests policy documents, contracts, SOPs, and workflows in various formats. Using NLP models and a dynamic rule engine that updates with new regulations, it automatically scans for regulation-specific clauses, identifies missing or non-compliant sections, and classifies risks.

Smart dashboards provide department-wise compliance status, risk exposure visualizations, and audit trails. When upcoming regulatory changes are detected, the system pushes real-time alerts to relevant teams and suggests mitigation actions.