Supadock develops AI-driven software agents that automate operational tasks in physical‑goods sectors such as trucking, freight brokerage, and warehouse management. The platform integrates with existing logistics systems to handle load matching, shipment tracking, and other routine workflows, reducing manual effort and improving efficiency. Supadock monetizes through subscription‑based licensing and usage fees tied to the volume of transactions processed by its AI workers.
Funding
Funding not disclosed
Founders
Product
Problem
Physical‑industry logistics firms—such as trucking fleets, freight brokers, and warehouse operators—must coordinate load matching, shipment tracking, and other routine workflows manually. This reliance on human operators creates high labor costs, delayed updates, and a greater likelihood of errors that erode operational efficiency.
Solution
Supadock delivers AI‑driven software agents, called “AI workers,” that plug into existing transportation‑management, warehouse‑management, and brokerage systems. The agents autonomously execute load‑matching, status‑tracking, and exception‑handling tasks using real‑time data feeds and domain‑specific machine‑learning models. By offloading repetitive actions to the AI workers, companies reduce manual effort, accelerate transaction processing, and achieve more consistent compliance with routing and regulatory rules. The platform provides a centralized dashboard for monitoring agent performance and integrates with standard APIs to preserve legacy system investments. Supadock’s architecture scales across fleets of any size, allowing operators to add or retire AI workers on demand without code changes.
Target Audience
Primary customers are trucking fleet managers, freight brokerage firms, and warehouse/shipping operators that need to automate high‑volume logistics workflows while retaining integration with their existing enterprise systems.
Features
- Orchestrated AI worker engine that executes rule‑based and predictive logistics tasks with low latency
- Pre‑built connectors for major TMS, WMS, and ERP platforms (e.g., SAP TM, Oracle Transportation, Manhattan) plus a REST/GraphQL API for custom integration
- Real‑time load‑matching algorithm that optimizes carrier‑load pairing based on capacity, distance, and cost constraints
- Automated shipment status updates with AI‑estimated time of arrival (ETA) and proactive exception alerts
- Compliance module that validates driver hours‑of‑service, weight limits, and hazardous‑material regulations in the workflow
- Multi‑tenant SaaS infrastructure with role‑based access control and audit‑trail logging for traceability
- Analytics dashboard offering KPI visualizations (utilization, on‑time delivery, cost per mile) and downloadable reports
- Low‑code configuration UI enabling operations teams to define new task templates without developer intervention