Catch failures days ahead, not hours.
AI-driven equipment health monitoring using vibration, current-signature, and thermal analysis — calibrated against your equipment baseline, not generic templates.
Time-based maintenance over-services good equipment and under-services bad equipment.
A six-month service interval treats a healthy motor and a dying motor identically. The healthy one gets a service it did not need. The dying one fails three weeks before its scheduled visit. Both outcomes cost money; only one is visible to finance.
And the third option — react when something breaks — costs more than both, paid in production-loss minutes you cannot make back.
Equipment that tells you when it is changing.
We instrument critical assets with vibration, current, and thermal sensors. AI models learn each asset's baseline over a calibration period. When the baseline shifts in a way that historical data says precedes failure, the system flags it — with a confidence score and an estimated action window.
The output is not a black box. Maintenance leads see what changed, when, and how confident the model is. Action stays with humans; signal becomes their job, not their search.
“Predict problems before they stop your production.”
What you can expect.
Outcomes are measured against deployed-site baselines, not vendor specs.
Calibrated to your assets
Models trained on your equipment's baseline during a 60-day learning window. Not a generic motor template.
Actionable lead time
Average prediction window across deployed sites: 9–14 days before the failure mode the model flagged.
Confidence scores, not alarms
Every alert ships with a confidence level. Maintenance leads triage by signal strength, not noise.
Works with what you have
Sensors are vendor-neutral. The platform integrates to your CMMS — Maximo, SAP PM, UpKeep, or open-source.
Industries where this changes the day.
Manufacturing plants
Automotive
Process industries
Packaging lines
Utilities
Heavy machinery
Continuous production
Why Webtech Space Centre specifically.
Engineering-led, not data-science-led
Our maintenance team includes engineers who have run the equipment we instrument. The model gets calibrated by people who know what a degrading bearing actually sounds like.
Honest about what the model does not know
When the model is uncertain, the dashboard says so. We do not surface fake confidence to make the product look smarter.
Tied to a CMMS, not a separate workflow
Predictions land as work orders in the system maintenance teams already use. No second tool, no new login, no parallel process.
Let's see how Predictive Maintenance fits your line.
Tell us a bit about your operation and we'll come back within one working day with a structured demo and an honest fit assessment. Demo can be remote or on-site at our Delhi studio — whichever suits you.
- Demo runs against a real production line, not a slide deck
- Fit assessment is honest — including when our solution is not the right answer
- Indicative pricing and timeline shared in the same meeting