AI Automation

Meet your new operations assistant.

ATLASfusion's AI Agent Engine watches your live operation, reasons about each event in context and acts on it, all according to your instructions.

As autonomous as you want. Always under your control.

Three ways in

Ask it. Task it. Trust it.

Ask it

Live chat

Ask in plain language and get answers from the live operation: where things are, what moved, what needs attention next. The AI has the map, the history and the workflow in front of it.

Task it

Delegation via @atlas

Mention @atlas in any ticket or conversation and the job is handed over. The agent does the legwork, takes the follow-up actions and reports back when it's done.

Trust it

Continuous overwatch

Agents watch the operation around the clock and act the moment a rule fires: raising tickets, alerting the right people and chasing the follow-up, always within limits you set.

Why you can trust it

Grounded context

On every rule trigger the agent is handed live operational state: the asset, the zone, recent movement, the people on shift, the rule it is running. Reasoning happens against real data your team is also looking at.

Tool-use loop

The agent works step by step, calling explicit tools to raise tickets, assign work, send push, email, SMS and Teams messages, hit your webhooks and schedule its own follow-up checks. Every tool call is logged.

Bounded autonomy

You set the guardrails: which tools the agent may call unattended, which require a human in the loop, and limits on how far each run can go. Every reasoning step and every tool call is logged. Move the autonomy dial up or down at any time.

The Agent At Work

Four scenarios, four Agent runs.

ATLASfusion Agents work by observing, reasoning, acting and following up.
Here's four operational scenarios to show how that looks in practice, across different industries and use cases:

A late-model SUV with a courtesy-vehicle decal pulling back into a dealership service drive in warm afternoon light.

Courtesy car overdue

Mid-afternoon at a busy dealership. One of thirty courtesy cars on loan is forty-five minutes past its scheduled return. The advisor who issued the loan is in a service consultation and has not noticed.

The agent picks it up first. It pulls the loan record, reviews the car’s last twenty-four hours of location history and cross-references the customer’s previous loans, all returned on time. The latest position fix is fourteen kilometres from site, near a shopping centre, last seen thirty-eight minutes ago. It reads this as a routine overrun.

It opens a task against the duty service advisor (the original advisor stays uninterrupted), sends the customer a courteous SMS offering a late return, posts a one-line summary into the dealership’s operations Teams channel and schedules a re-check in thirty minutes.

Twenty-two minutes later the car re-enters the site geofence. The agent verifies the loan record is closed, marks the ticket resolved with an audit note and stands the re-check down. The whole loop runs without pulling the advisor out of the customer consultation.

Your AI, your rules

Bring your own AI subscription.

The agent runs on your own Anthropic API key, not ours. That keeps AI spend on your existing AI budget, under your existing corporate cost controls.

It also means every model call goes through your Anthropic account, so your data protection, residency and integration policies apply directly to the agent working on your operation.

How much more could you achieve?

What could you get done if you knew where everything was, and routine tasks were handled automatically? Let's help you find out.