How AI Turns Rate Confirmations Into Ready-to-Dispatch Loads

The Manual Load Entry Problem

Every rate confirmation that lands in your inbox has to become a load in your TMS. Today, that means a dispatcher opens the PDF, reads the pickup and delivery addresses, the rate, weight, equipment type, and reference numbers - then types it all into McLeod, TMW, TruckingOffice, or whatever system you run. A single load takes 5–10 minutes. Multiply that by dozens of loads per day, and you're burning hours on data entry.

Manual entry is also prone to typos. A wrong ZIP code, a transposed rate, or a missed reference number can cause routing errors, billing disputes, or failed broker packets. AI load building eliminates that bottleneck by extracting structured data from rate confirmations automatically and pushing it directly into your TMS via API.

What AI Extraction Captures

MegaTMS's AI reads rate confirmations - whether they're PDFs, images, or scanned faxes - and extracts the fields your TMS needs. That includes customer and broker info, pickup and delivery addresses with appointment times, freight details like rate and weight, equipment type, commodities, reference numbers, and special instructions.

• Customer / broker - name, MC number, contact info.
• Pickup & delivery - full addresses, appointment windows, contact names and phone numbers.
• Freight details - rate, weight, equipment type, commodity description.
• Reference numbers - broker load #, PO number, BOL number, and any other identifiers.
• Special instructions - lumper, detention, temp requirements, hazmat notes.

Multi-Stop Load Handling

Simple point-to-point loads are straightforward. But many rate confirmations describe multi-stop routes: pick up in Dallas, drop in Memphis, pick up again, deliver in Chicago. The AI parses these complex layouts and builds a structured sequence of stops - each with its own address, appointment time, and role (pickup vs delivery).

That structure is what your TMS needs to optimize routing and assign drivers. Instead of a dispatcher manually breaking down a multi-stop rate con into individual legs, the AI does it in seconds. The result is a load object with an ordered list of stops, ready for dispatch.

TMS Integration via REST API

Once a rate confirmation is extracted, MegaTMS exposes the load data through a REST API. Your TMS or dispatch system can poll the GET /api/v1/loads endpoint to fetch new loads as they're created. Authentication uses an API key passed in the request header.

For webhook-style polling, use the since parameter to request only loads created or updated after a given timestamp. That keeps your TMS in sync without re-fetching everything on each call.

Error Handling and Human Review

AI extraction isn't perfect. Handwritten notes, poor scans, or non-standard rate con formats can leave the model uncertain about a field. When that happens, MegaTMS flags the extraction for review instead of pushing incomplete data to your TMS.

Dispatchers see a review queue where they can correct or fill in any uncertain fields before the load is finalized. Edits are saved and used to improve future extractions. The workflow stays human-in-the-loop for edge cases while automating the bulk of routine load entry.

Once a load is approved, it can be pushed to your TMS via API or exported for manual import. Either way, you're dispatching from clean, structured data instead of re-typing from PDFs.