New Order Detected
- Extract order details
- Validate information
- Create order
AI Agents
AI Agents
Logistics never sleeps. Neither do your AI agents.
Your operation keeps running long after the office lights go off. Orders arrive overnight, integrations fail, stock levels change and deliveries run into unexpected delays. By the time your team is back at their desks, a small issue can have become a much bigger problem.
- Always watching
Agents run inside the modules you already use, monitoring orders, stock, routes and deliveries around the clock.
- They act, within your rules
Each agent works out what needs doing and does it inside the limits you set — or brings it to the right person when a decision is needed.
- Your team gets the exceptions
Less time on dashboards, chasing updates and re-typing orders from PDFs and emails. Routine work keeps moving on its own.
AI Agents · keeping logistics moving
Route optimised
Real-time visibility
Exception detected
Automated action
Delivery on track
The agents
One for every part of the job, from order to doorstep.
Each agent works inside one module. Run the ones that match the problems you actually have.
Low Stock Alert
- Item: A-1042
- Current: 12
- Reorder at: 30
Stock Watch Agent
Optimised Run
- 14 stops
- 112 km
- 22% shorter
- On-time: 98%
Run Planner Agent
Delivery Exception
- Job: DEL-4487
- Issue: Delay (Traffic)
- ETA: +45 min
Delivery Exception Agent
Customer Update Sent
Your delivery (DEL-4487) is now on the way.
New ETA: 2:30 pm
Customer Update Agent
Systems In Sync
- ERP · synced 2 min ago
- eCommerce · live
- DMS · live
Integration Watch Agent
How they work
Useful because they are narrow, trusted because they show their working.
One job each
An agent that does one thing is easier to trust, easier to audit and easier to turn off than an assistant that claims to do everything.
Propose, then act
Agents start in draft mode: they prepare the action and a person approves it. You decide, one agent at a time, which have earned the right to act alone.
Shows its working
Every action is logged with what the agent saw and why it acted. When someone asks why a customer got that message, there is an answer.
Your data, your platform
Agents run inside your own Synvora environment on Australian-hosted data, against the records you already keep.
How is this different from SynvoraBot?
SynvoraBot is the side you talk to — you ask it a question about orders, stock or deliveries and it answers. Agents work the other way around: they run in the background on a trigger and come to you. Same underlying platform, different job.
Will an agent do something without us knowing?
Not unless you turn that on. Every agent starts in draft mode, where it prepares the action and waits for a person. You can promote individual agents to act on their own once you trust them, and reverse that at any time.
What happens when an agent gets it wrong?
It will, occasionally — any system that reads a messy PDF or predicts traffic will be wrong sometimes. That is why actions are logged and reversible, and why anything customer-facing is drafted rather than sent. Start with draft mode on the agents where a mistake would be visible.
Do we need all of them?
No. Most clients start with one, against whichever problem is costing them the most — usually order entry or delivery exceptions. The rest can wait until the first one has proved itself.
What do you need from us to make this work?
Reasonably consistent data in the module the agent watches, and a decision about which actions may happen automatically. If your data is messy, the consulting audit will say so before anyone spends money on AI.
Does this replace people?
It replaces the checking, not the judgement. The work that disappears is watching a screen for something to go wrong and re-typing information between systems. The decisions stay with your team, which is the point of the approval step.
Start with one agent, on one real problem.
Tell us where your team loses the most time. We’ll tell you honestly whether an agent helps, or whether something simpler would.