Knowing a Machine Will Break, Before It Does
We built a predictive maintenance system for a manufacturing company that was losing hundreds of hours annually to unexpected equipment failures. The system now flags problems up to 3 weeks in advance, and has paid for itself 8.5 times over.
16
Weeks to Deploy
500+
Sensors Integrated
35
Critical Assets Monitored
5TB+
Data Processed Monthly
Breakdowns were costing more than the machines themselves
- —Unexpected failures caused 120+ hours of production downtime every year — with no warning and no plan.
- —Emergency repairs cost 3–5x more than planned maintenance, draining budget and catching teams off guard.
- —Fixed maintenance schedules meant servicing healthy equipment unnecessarily while missing real problems developing elsewhere.
From reactive to ahead of the problem.
- ✓Connected 500+ existing sensors to a central platform that analyzes equipment behavior in real time, 24/7.
- ✓Trained machine learning models on 3 years of failure history — so the system recognizes early warning patterns long before a human would notice.
- ✓Built a dashboard that shows maintenance teams exactly what needs attention, when, and why — ranked by urgency.
System Dashboard
Equipment Health
92%
Predicted Failures
3
Sensors Online
498/500
Critical Alerts
What We Built
Four capabilities that turned a reactive maintenance team into a proactive one.
The system flags potential equipment failures up to 3 weeks before they happen, giving teams time to plan maintenance without interrupting production.
500+ sensors stream data continuously. Every piece of critical equipment has a real-time health score the operations team can see at a glance.
Not every alert is equal. The system ranks warnings by urgency — so maintenance teams know exactly what to fix first and what can wait.
The models retrain continuously on new data and confirmed failures, improving prediction accuracy as they learn the specific patterns of each machine.
The Results
73%
Reduction in unplanned downtime
45%
Decrease in maintenance costs
92%
Failure prediction accuracy
8.5x
Return on investment
Before this system, we were always scrambling after something broke. Now we know what's coming and we plan around it. Emergency repairs are rare, and our maintenance budget has dropped significantly.
Tired of unexpected breakdowns?
We build predictive systems that give operations teams visibility into what's coming, so maintenance happens on your schedule, not the machine's.