AI Dispatcher captures the decision-making logic locked in your team's experience — from chat history, calls, and screen recordings — and turns it into a scalable system that handles routine operations 24/7.
Standard SLA checks, status follow-ups, and driver coordination happen by hand, every day, by your most experienced people.
A new dispatcher has no access to accumulated decisions. Every error during the learning period is paid for by your operations and your clients.
Management doesn't see which decisions get made, why, or how consistently. There's no data to optimise from — only outcomes after the fact.
AI Dispatcher is an AI-agent platform built for logistics. It extracts your team's operational expertise from chats, calls, and screen recordings, and turns it into a scalable system that handles routine operations, monitors SLA compliance, and escalates anomalies with full context. One dispatcher manages what used to require five.
See what no chat log or call record can show.
The platform analyses dispatcher screen recordings to extract real working patterns: the order of decisions, the hidden steps, the context switches that never make it into any system. This is expertise that cannot be captured any other way.
Turn unstructured experience into company-owned logic.
Identified patterns are decomposed into validated scenarios: reactive (incoming requests) and proactive (trigger-based monitoring). Each scenario is reviewed with your experts before deployment.
Choose the right level of automation for every scenario.
In Augmented mode, the AI prepares decisions for dispatcher approval, multiplying capacity without removing human judgement. In Autonomous mode, fully deterministic scenarios run without human involvement, with automatic escalation for any deviation.
AI Dispatcher continuously monitors active shipments for four categories of critical deviation. Each type has its own urgency level and triggers a specific action — from an informational alert to immediate escalation.
The driver is too far from the pickup point relative to the remaining SLA window. The system cross-references geolocation, distance, and road conditions, and signals before the breach, not after.
The driver began moving but has unexpectedly stopped. Without automated detection, the dispatcher only discovers this at the next manual screen refresh. The system flags it the moment it happens.
The driver has jumped stages in the system, either an interface error or an attempt to bypass controls. The system detects the sequence break and initiates verification.
The driver's physical location does not match their declared status. This requires immediate verification and is escalated with the driver's actual coordinates, last communication, and remaining SLA time.
The agant analyses your team's chat history, call logs, and screen recordings to map real decision-making patterns — not what people say they do, but what they actually do.
Identified patterns are decomposed into concrete scenarios with clear rules, reviewed and approved by your operations experts before any automation is deployed.
AI agents connect to your communication channels and TMS. They handle context-aware decisions, monitor SLA compliance, and prepare escalations with full context.
One unified configuration model covers multiple clients and SLAs. A new client is a configuration, not a development project.
Expertise is formalised and owned by the company — a dispatcher leaving no longer means an operational crisis.
Every dispatcher works to the same digitised scenarios, and quality stops depending on who's on shift today.
Dispatchers decide, not diagnose. They receive reason for deviation, driver location, remaining SLA time, communication history, and a suggested action.
New dispatchers work alongside a system that already knows every standard scenario: time-to-productivity drops significantly.
Every decision is logged. Management sees a real picture of operations and optimises based on data, not subjective reports.
Growing order volume no longer requires proportional headcount growth in dispatch.
We analyze your operation type against our formalised scenario library. You see which workflows can be automated, what the impact looks like, and what implementation involves.
Get a Free Operations AuditYour most experienced dispatcher carries SLA logic for a dozen clients in their head, and you know it. Digital Dispatcher captures that logic before the next resignation, and scales it across your entire operations team.
Coordinating carriers, subcontractors, and clients across multiple SLAs means constant manual monitoring and firefighting. AI agent tracks every shipment and signals deviations before they turn into client complaints.
Writing separate automation rules for every client doesn't scale. AI Dispatcher uses a unified configuration model: one logic layer, per-client parameters for SLA thresholds, escalation levels, and timing.
| Criterion | Digital Dispatcher | Chatbots & RPA Tools |
|---|---|---|
| Knowledge capture | Extracts logic from screen recordings, chats, and calls | Requires manual rule definition by your team |
| Context awareness | Reads full conversation history, never asks a question already answered | Rigid triggers and timers with no memory |
| Anomaly detection | Monitors for SLA deviation, unexpected stops, status skips, location mismatch | No proactive monitoring — reacts only to explicit triggers |
| Escalation quality | Full context package: deviation reason, driver location, SLA remainder, suggested action | A flag on an order — dispatcher still has to diagnose |
| Scalability | One unified model covers multiple clients via configuration | Separate development required per client or workflow |
We'll audit your operations, map your scenarios, and run a pilot on your real workflows. You see results before you commit.