An AI agent watches every shipment like a hawk, automatically chasing updates from carriers and alerting your team only when something actually goes wrong.

Burt deploys an AI agent that continuously monitors shipment status across multiple carrier APIs, TMS platforms, and email inboxes using retrieval-augmented generation (RAG) to pull real-time data and LLM-based reasoning to interpret unstructured updates (e.g., carrier emails, EDI messages). The agent identifies exceptions—late pickups, missed scans, weather delays—and proactively escalates them to the appropriate human operator with context-rich summaries and suggested next actions. This eliminates the need for operations teams to manually check dozens of carrier portals and parse hundreds of daily status emails, dramatically reducing response times and preventing costly delivery failures before they cascade.

It's like having a tireless intern who refreshes every carrier tracking page every five minutes, reads every email, and only taps you on the shoulder when the building is actually on fire.

An AI agent reads, verifies, and scores every carrier's insurance, authority, and safety documents so your team doesn't have to play detective with PDFs.

Burt's carrier vetting agent ingests carrier onboarding documents—certificates of insurance, FMCSA authority records, safety ratings, and broker-carrier agreements—and uses LLM-powered document understanding to extract key fields, cross-reference them against authoritative databases (FMCSA SAFER, insurance verification APIs), and generate a composite risk score. The agent flags discrepancies (e.g., lapsed insurance, authority revocations, poor safety scores) and either auto-approves low-risk carriers or routes edge cases to a human reviewer with a detailed risk summary. This transforms a traditionally manual, error-prone, and time-consuming process into a fast, consistent, and auditable workflow—critical for brokerages and 3PLs managing hundreds or thousands of carrier relationships.

It's like a bouncer at a nightclub who instantly checks every ID, calls the DMV to verify it's real, and only bothers the manager when someone shows up with a fake mustache.

An AI agent figures out the best way to fill a truck and price a load by juggling dozens of constraints at once—faster and more accurately than a human planner.

Burt's load building agent uses a multi-agent architecture where specialized sub-agents handle distinct aspects of the problem: one retrieves real-time capacity and equipment availability from TMS and carrier systems, another analyzes historical lane pricing and market rate data, and a third evaluates physical constraints (weight limits, dimensional compatibility, hazmat restrictions, delivery windows). An orchestrating LLM synthesizes these inputs to recommend optimal load configurations and generate competitive quotes. The system learns from accepted and rejected quotes over time, fine-tuning its pricing models and constraint weighting. This replaces the traditional process where experienced planners manually juggle spreadsheets, rate sheets, and phone calls—unlocking faster quote turnaround, higher load utilization, and better margin capture, especially for mid-market brokerages that lack dedicated pricing analytics teams.

It's like a Tetris grandmaster who also happens to be an economist—fitting every box perfectly while making sure you're not leaving money on the table.