AI Dispatcher monitors every delivery, handles up routine driver communication automatically, and catches deviations before they breach SLA.
Status checks, ETA requests, address clarifications — the same conversations dozens of times a day. Your dispatchers spend most of their shift on cases that follow the same script.
Bots that don't read prior conversation break driver trust so they stop responding. Dispatchers end up duplicating every message manually.
Without continuous monitoring, a driver who stopped or drifted off route is discovered only at the next manual screen refresh. By then, the SLA is already breached.
AI Dispatcher reads the full context of every active delivery, and handles routine scenarios autonomously. When a deviation happens, it escalates with everything already prepared. Your team decides, not diagnoses.
Reads the full conversation before sending anything.
The agent knows what the driver already said. It doesn't ask about an order already reported as delayed, but acts on that information and sends a message that fits the situation.
Catches problems before the dispatcher opens the screen.
The system continuously monitors geolocation, order status, and communication history for four deviation types. Each triggers a different action: from an informational alert to a full escalation package.
The right level of automation for every scenario.
Routine cases run autonomously with no dispatcher involved. Complex or ambiguous situations get a complete decision package prepared and waiting for approval. You choose the mode per scenario.
AI Dispatcher continuously monitors every active delivery for four categories of critical deviation. Each has its own urgency level and triggers a specific action.
The driver is too far from the pickup point relative to the remaining SLA window. The system cross-references geolocation, distance, and traffic conditions, and signals before the breach, not after.
The driver started moving but suddenly 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 skipped stages in the system — it's 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, but what they do.
Identified patterns are decomposed into concrete scenarios, reviewed and approved by your operations experts before any automation is deployed.
AI agents connect to your communication channels. They handle driver communication, monitor deviations, and prepare escalations.
Growing delivery volume is a configuration change, not a hiring decision. A new city or zone is a parameter, not a development project.
80% of routine driver scenarios close autonomously. Your dispatchers focus on the 20% that actually need human judgment: complex exceptions, escalations, client calls.
Context-aware messages rebuild driver trust. The agent doesn't ask what the driver already answered. Response rates recover, and dispatchers stop maintaining parallel communication threads.
The dispatcher receives a deviation with cause, current location, remaining SLA window, and a suggested action — not just a flag on an order they now have to diagnose themselves.
Every time volume grew, the answer used to be another dispatcher. AI Dispatcher breaks that equation — growth is a configuration change, not a hiring cycle.
We analyze your operations against our formalised scenario library - you get an insight into where automation has the highest impact. No commitment.
Get a Free Operations AuditYour dispatchers track dozens of concurrent orders, each with their own driver, status, and SLA clock. Manual monitoring means deviations are found late or not at all.
The bot sent the same question whether or not the driver had already answered. Trust broke. Dispatchers started duplicating manually. The automation made the workload worse.
Every new city, zone, or client triggers a new headcount request. You need a way to scale operations that doesn't scale your payroll at the same rate.
| Criterion | AI Dispatcher | Chatbots & RPA Tools |
|---|---|---|
| Context awareness | Reads full conversation history, never asks a question already answered | Rigid timers and triggers with no memory of prior messages |
| Automation | Closes up to 80% of typical driver scenarios autonomously | Executes predefined scripts; fails on any deviation from the template |
| 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, location, SLA remainder, suggested action | A flag — dispatcher still has to diagnose the situation |
| Driver trust | Context-aware messages restore driver engagement | Context-blind messages are ignored; dispatchers duplicate manually |
Start with a free express audit. We show you the automation potential for your type of operation.