Catch the failure before it catches you.
Every unplanned stop, every wasted spare, every emergency call-out is margin walking out the door. Decisyon's AI agents predict the failure, find the root cause, suggest the fix, create the work order and integrate with the maintenance workflow.
From firefighting to foresight — with the fix already in hand.
Most plants live somewhere between reactive ("it broke, go fix it") and calendar-based ("swap the part whether it needs it or not"). Both burn money. Predictive maintenance finally sees what's coming. Prescriptive maintenance closes the loop — telling your team exactly what to do, when, and with which parts.
That shift attacks the three silent killers of plant profitability: avoidable downtime, bloated spare-parts inventory, and labor stuck firefighting instead of improving.
Five leaks, every single shift.
A line goes down mid-shift and the whole day's plan goes with it.
You're swapping parts that still had life left — or finding the damage after it's already done.
An alarm screams. Nobody knows what's actually wrong. Hours disappear into diagnosis.
Every site runs maintenance its own way. Best practices never travel.
Maintenance spend rises, asset life shortens, and the CFO starts asking hard questions.
Four steps. No rip-and-replace. Your sensors stay where they are.
- 1. Connect what you already have. The Decisyon Smart Gateway plugs into the vibration, heat, pressure, and acoustic sensors already on your line — no equipment overhaul.
- 2. Learn what "normal" looks like. AI agents establish a healthy baseline for each asset from its own history. Every machine becomes its own benchmark.
- 3. Catch the anomaly humans miss. Predictive agents flag the early deviation a tech walking the floor — or a fixed-threshold alarm — would never see.
- 4. Prescribe and dispatch. The risk is named, a work order is opened in your CMMS or ERP, and the right technician shows up with the right parts and the diagnostic data already in hand.
From "we think something's wrong" to "here's the asset, the fault, the fix, and the tech on the way."
AI agents amplify your existing sensor data with high-fidelity synthetic signals — production-grade accuracy in weeks, not the years a traditional model would need.
Prescriptive agents classify the exact fault, the severity, and the asset — so the first person on the scene already knows what they're walking into.
Technicians see why the model made the call. Trust builds. Adoption sticks. Audits get easier.
A built-in MLOps feedback loop learns from your technicians' fixes — the agents improve as your team teaches them.
An LLM-powered agent writes the work order with the right parts, the right safety notes, and the right tech — delivered the moment the risk is real.
Maintenance stops being a cost center. It starts protecting throughput.
- · Months of model training and custom integrations before anything runs
- · Alerts and dashboards — diagnosis is still on your team
- · Black-box scores no one trusts
- · Static models that drift as machines age
- · Rigid architectures that can't keep up with your plant
- · Pre-trained agents on a low-code platform. Live in weeks.
- · Prescriptive work orders with parts, steps, and the right technician attached
- · Transparent model reasoning and confidence — visible to every tech
- · Self-improving MLOps loop that learns from every fix
- · Built on Decisyon App Composer — adapts to your line, not the other way around
See prescriptive APM running on one of your assets.
Real sensors. Real failure signatures. Real work orders. Inside an hour you'll see what the agents catch, what they prescribe, and what it would have cost you to find out the old way.
One plant. One use case. Real data.
Clear success criteria. Walk away on day 14 if it doesn't move the number.