Catch the failure
before the line stops.
Jemba learns each asset’s normal behaviour from the data you already collect and flags anomalies before they become breakdowns — in under 2 seconds, with no data scientist and no threshold-tuning marathon.
Reactive maintenance is expensive.
Threshold alarms cry wolf.
Fixed thresholds fire late and often — you either drown in false alarms or miss the drift that matters. The signals that predict a failure are already in your data; they’re just spread across hundreds of variables no operator can watch at once. Jemba watches all of them, and only speaks up when it counts.
Learns normal.
Flags the abnormal.
An unsupervised model builds a fingerprint of healthy operation per machine — no labelled failure history required — then scores every new reading against it in real time.
- Unsupervised — no labelled failures needed to start.
- Anomaly score in under 2 seconds, streamed live.
- Handles drift automatically — the model stays honest as the line changes.
Trusted on the floor by 450+ factories.
Jemba is built by TeepTrak — the production-monitoring platform manufacturers already run on the line, proven across 30+ countries.
From sensor to save,
in four steps.
Connect
Stream from TeepTrak, PLC, OPC-UA, a CSV or the API.
Learn
Jemba models normal behaviour per asset — automatically.
Alert
Anomalies surface with context, before failure.
Act
Route to a work order over CMMS, Slack or email.
One platform.
Four ways to win on the line.
Quality correlation
Find the root cause of defects in minutes by isolating the parameters that really drive quality.
Energy monitoring
Model consumption against production to expose waste and make energy controllable.
OEE optimisation
Understand the why behind your losses, on top of the TeepTrak stream.
Stop the next breakdown before it starts.
Book a 15-minute demo or start a free trial. 48-hour setup, no data scientist required.
Frequently asked questions
How does Jemba predict machine failures?
Jemba learns each machine’s normal behavior from your sensor data and flags anomalies before they become breakdowns — with no fixed thresholds to tune.
Does predictive maintenance need new sensors?
Usually not. Jemba works with the sensors, PLCs and historians you already have in place.
How is this different from threshold alarms?
Threshold alarms tend to fire too late or too often. Jemba’s anomaly detection catches the subtle drift that fixed limits miss.
How fast can we see results?
Setup takes about 48 hours, and the model starts surfacing anomalies as soon as it has learned your baseline.
