AI agents built for logistics ops
Automate the repetitive coordination work — tracking updates, exception handling, freight quoting and carrier comms — with agents that plug into your TMS and act in real time.
What this means for your operation
Logistics runs on coordination: chasing ETAs, re-quoting freight, replying to where-is-my-shipment emails and resolving exceptions across carriers, customers and warehouses. Most of it is high-volume, rules-driven and brutal on margins when done by hand.
An AI agent can monitor shipment events, draft proactive status updates, flag at-risk loads, generate freight quotes from your rate tables and escalate true exceptions to a human. It works inside your existing TMS and EDI flows rather than replacing them.
We build these as production systems with retries and observability because a dropped status update is a real cost. The outcome is fewer manual touches per shipment, faster quote turnaround and customer comms that never go quiet.
What we build
Track-and-trace agent
Monitors carrier events and sends proactive, branded status updates before customers have to ask.
Exception handling
Detects delays, missed pickups and damage flags, then triages and routes to the right human.
Freight quoting
Generates quotes from your rate tables and lane history in seconds, with margin guardrails.
EDI & TMS integration
Connects to your transport management system and EDI feeds so the agent reads and writes real data.
Carrier comms
Drafts and sends carrier follow-ups, appointment requests and tender responses.
Ops dashboard
A live view of what the agent handled, what it escalated and where exceptions cluster.
Implementation details
| Capability Parameter | System Specification |
|---|---|
| Integrations | TMS, EDI (214/204/990), carrier APIs and tracking webhooks |
| Actions | Send updates, generate quotes, create exceptions, escalate to humans |
| Guardrails | Margin floors on quotes and approval gates on financial actions |
| Stack | Serverless event processing, vector store for SOPs, relational store for state |
| Engagement | Pilot on one lane or workflow, then expand across the network |
| Typical budget | ₹25L–₹50L / $25k–$60k for a production deployment |