Smart evaluators watching your production lines
The signals machines already generate can warn the floor before the line stops
What it solves
On a production line, a stoppage is paid for immediately: orders slip, operators stand idle, a whole day is lost to a fault that, looking back, had given some warning. Those signs were in the MES data or the machine logs, but at the time nobody was reading them.
The issue is that they stay scattered across different systems and only become readable once a technician goes looking on purpose, usually after the stoppage has already happened. The data itself isn't the problem, it's there: whoever is on the line has no way to notice it beforehand.
This scenario is for make-to-order manufacturers and anyone in manufacturing where every hour of downtime is a direct cost: an AI agent that watches the signals you already have, compares them against the plant's normal behaviour, and warns the floor the moment something starts to change.
How it works
The agent reads the signals you already have
It connects to your MES, machine logs and existing sensors and watches them continuously, without stopping production or adding any instrumentation. It works on the data your plant already generates every day.
It compares the current state against normal operation
When a parameter drifts away from the plant's usual behaviour, the agent assesses whether it's a slow drift heading towards a stoppage or a routine spike from a machine that is simply working hard. It reacts to the first, leaves the second alone.
It warns the operator where they already work
The alert arrives in the tool the floor already uses, even if that's just a notification on a phone app, and explains what it saw and what it's based on. The operator decides: stop for a check, call maintenance, or let it run if they know it's a false alarm.
Why it stays under control
- Every alert states which signals it's based on and what it flagged as anomalous, so the operator can check it instead of trusting it blindly
- The agent works inside the MES, logs and dashboards you already have: no parallel system to learn and no data leaving your servers
- Thresholds, rules and alert priorities are yours to set and change as the floor changes; a record is kept of who changed them and of every alert raised
What changes
- The floor stops discovering problems after the stoppage and starts seeing them coming, with room to act
- Operators use the alerts and trust them, because they're explained and arrive where people already work, not in yet another dashboard to keep an eye on
- What your most experienced technician senses by instinct when a machine is about to fail no longer stays only in their head: the system recognises it and flags it too
What we don't promise
- We aren't OT or IoT specialists and we don't sell sensors or hardware: we work on the software, on how those signals become readable and usable on the floor
- We don't promise to eliminate stoppages: an agent spots trouble ahead when the signals are there, it doesn't predict sudden failures that leave no trace in the data
- We don't put in a black box that decides for you: the AI proposes and explains, the decision and the action stay with the operator
Frequently asked questions
The data is already in the MES, why do we need an AI agent
Today, someone only looks at those signals after the line has already stopped. The agent watches them while the machine is running and turns them into a useful warning while there's still room to act.
How do I know why an alert fired, so I'm not just trusting a black box
Every alert states which signals it's based on and what it flagged as anomalous, so whoever is on the floor can check it and judge whether it makes sense. The thresholds and rules stay yours, and every alert is logged.
Won't operators just ignore yet another complicated system
The same rule applies to us: nobody opens a clunky terminal. That's why the warning arrives as a clear notification on a phone app that everyone already knows how to use. The value only exists if the floor actually uses it.
How do we know if this is worth it before investing
You start with the Inception package: 2-3 weeks at contained investment where we read your real signals and see where AI adds value and where it doesn't. At the end you have concrete grounds to decide, without being tied into a long project.
At a glance
Tags
Does this scenario sound familiar?
We start with an Inception package to see, on your actual case, whether and where it makes sense.
Book a callTell us where you're stuck
A fragile prototype, a burdensome legacy codebase or unpredictable delivery: that's where we start