Fix it before
it breaks.
Predict equipment failures weeks in advance. Schedule maintenance around production, not around emergencies. Extend asset life and protect uptime — automatically.
Predictive maintenance that protects your operations
AI Maintenance Assistant continuously monitors every critical asset, predicts failures weeks before they happen, and turns emergency repairs into planned interventions — automatically.
Predict failures
AI watches every sensor signal, vibration trend, and performance drift — flagging equipment heading for breakdown weeks before it happens.
Cut downtime
Schedule maintenance around production windows, not around emergencies. Unplanned downtime drops up to 50% in the first year.
Extend asset life
Right-timed interventions stop minor wear from becoming catastrophic failure — extending the useful life of every major asset.
From reactive repairs to predictive intelligence
Each capability replaces guesswork and calendar-based maintenance with evidence-based, data-driven decisions.
See breakdowns coming weeks in advance
The AI learns the unique signature of every asset and detects subtle changes that signal an emerging failure — long before traditional inspection would catch it.
- Early-warning alerts up to 12 weeks before failure
- Specific failure mode identification (bearing wear, misalignment, lubrication)
- Confidence scoring on every prediction
- Recommended actions with priority and urgency
One health score for every asset, updated continuously
Every machine, pump, motor, and conveyor gets a live health score combining sensor data, performance trends, and operational context — so you know which assets need attention right now.
- Continuous health scoring across every critical asset
- Vibration, temperature, current, and throughput monitoring
- Trend analysis with historical baselines
- Portfolio-level dashboards with drill-down to individual assets
Maintenance that fits around production, not the other way around
The platform recommends optimal maintenance windows based on actual asset condition, production schedule, and crew availability — eliminating both over-maintenance and emergency callouts.
- Risk-based scheduling that replaces fixed-calendar plans
- Production-aware maintenance window optimization
- Auto-generated work orders with parts and labor estimates
- Integration with existing CMMS and ERP systems
The metrics your reliability team has been waiting for
MTBF, MTTR, OEE, root-cause analysis, and asset lifecycle cost — all calculated automatically and surfaced in dashboards your engineers and your executives actually use.
- Real-time MTBF, MTTR, OEE, and availability tracking
- Root-cause analysis with failure-mode classification
- Asset lifecycle and total cost of ownership reporting
- Executive dashboards with reliability KPIs by site
From sensor signal to scheduled fix
Four steps run continuously on every monitored asset — automatically, around the clock.
Monitor
Sensors, telemetry, and operational data stream into the platform continuously — no manual rounds needed.
Detect
Machine learning identifies anomalies, drift, and emerging failure patterns before they trip standard alarms.
Predict
The AI estimates time-to-failure and recommends the specific repair, parts, and timing window.
Schedule
Auto-generated work orders flow into your CMMS, scheduled around production. Crews fix the right thing at the right time.
Built for asset-heavy operations
AI Maintenance Assistant pays back fastest in industries where unplanned downtime is measured in tens of thousands per hour.
Manufacturing
Multi-line plants where every minute of unplanned downtime stops the entire production line.
Energy & Utilities
Power plants, refineries, and grid infrastructure where asset failures cascade into regulatory and reliability exposure.
Mining & Heavy Industry
Remote sites where parts and crews can’t be deployed on short notice — predictive scheduling is non-negotiable.
Data Centers
Cooling systems, UPS, generators, and switchgear where reliability uptime SLAs drive every contract.
Warehousing & Logistics
Conveyors, sortation systems, and forklift fleets where availability shapes pick rates and dock turnaround.
Industrial Services
Field-services operators managing distributed asset portfolios with SLA-driven uptime commitments.
Common questions
How accurate are the failure predictions?
Most customers see 90%+ prediction accuracy within 60-90 days, once the AI has learned the unique patterns of your specific equipment. Every alert comes with a confidence score and the data signals driving the prediction — so your engineers stay in control of decisions.
Do we need to install new sensors on all our equipment?
No. The platform works with the sensors and SCADA data you already have — vibration, temperature, current draw, throughput, run-time. We add new sensors only where critical assets currently have blind spots, and only after the pilot proves ROI.
How quickly do we see results?
Most teams are operational in 4-8 weeks. Asset health scoring begins immediately. Predictive models reach full accuracy after 60-90 days of learning. First documented downtime savings typically show up in quarter two.
Does it integrate with our existing CMMS?
Yes. The platform integrates with Maximo, SAP PM, Infor EAM, Fiix, UpKeep, and most other CMMS platforms via standard APIs. Work orders generated by Theta flow directly into your existing maintenance workflows.
How does it handle legacy or unsensored equipment?
For legacy assets, the platform combines available operational data (run-time, output, energy draw) with historical maintenance records to surface failure patterns. Low-cost retrofit sensors can be added selectively where the asset criticality justifies it.
Can we start with one site or one asset class?
Yes. Most engagements start with a focused pilot on one site or critical asset class over 60-90 days, with a clearly defined ROI target. Once results are proven, scaling to the full portfolio is rapid because the models, integrations, and workflows are already established.
Stop reacting. Start predicting.
Book a 30-minute demo. We’ll walk through your asset portfolio and show exactly how Theta would surface the next failure — before it surfaces in your control room.