Automate Your Brokerage Beyond Logistically TMS

A typical brokerage uses Logistically TMS as its central hub. However, the workflow is rarely contained within one system. The real work happens in the gaps between Logistically, a load board like DAT or Truckstop, an accounting platform like QuickBooks, and a compliance tool like Saferwatch. There are no deep, workflow-level integrations connecting these platforms out of the-box.

Consider this common scenario for a 15-person team: A dispatcher receives a load tender as a PDF in an email. They must open Logistically, open the PDF, and manually type over 20 distinct fields to create the load. Then, they log into DAT to post the load, re-typing much of the same information. As carriers respond, the dispatcher manually looks up each one in Saferwatch to check their MC number and safety rating. Once a carrier is selected, the dispatcher manually creates a rate confirmation and updates both Logistically and a tracking spreadsheet. This entire sequence takes 20-30 minutes of low-value work for a single load.

The structural problem is that these are all separate systems of record with their own data models. Logistically TMS is not designed to be an intelligent document parsing engine. DAT is not designed to be a carrier relationship management tool. QuickBooks does not natively understand freight brokerage concepts like lumper fees or fuel surcharges. Each tool does its one job well, but the connective tissue is your team's manual labor and time.

This manual process does not scale. Hiring more dispatchers just adds more cost and more potential for data entry errors. A single transposed digit in a rate confirmation or destination zip code can cost thousands of dollars to fix. The core business process relies on error-prone, repetitive human action instead of reliable system-to-system communication.

The first step is a workflow audit. Syntora would map every step of your process from the moment a shipper email arrives to the final invoice payment in QuickBooks. This involves reviewing your exact document formats and identifying the specific API endpoints or access points for each system in your stack. You receive a technical blueprint that outlines the proposed data flow and the integration points before any code is written.

The core of the solution would be a set of Python services deployed on AWS Lambda. For inbound emails, a service would use the Claude API to parse unstructured text and PDFs, extracting fields like origin, destination, weight, and equipment type with over 99% accuracy. This structured data then populates a new load in Logistically TMS via its API. Another service would use the DAT API to automatically post the load and monitor for interested carriers. Pydantic models would enforce strict data validation at every step to prevent errors.

The delivered system runs autonomously in the background. Your dispatchers would receive a Slack notification with a pre-vetted carrier and a one-click approval link. Upon approval, the system generates the rate confirmation and updates Logistically automatically. You receive the complete Python source code in your own GitHub repository, a runbook for maintenance, and a dashboard on Supabase to monitor processing times and accuracy.