Predictive Maintenance
Catch failures before they happen — turn sensor and maintenance data into prioritized, explainable alerts your team can plan around.
The problem
Unplanned downtime is the most expensive thing on the floor, and calendar-based maintenance wastes parts and labor. The sensor data to predict failures exists — it’s just not being used.
What it includes
The capabilities we build into this solution — engineered for production, not a demo.
Failure prediction
Model time-to-failure from sensor, telemetry, and history data.
Anomaly detection
Spot the early signatures of a problem before it cascades.
Maintenance prioritization
Rank what to service first by risk and impact, not the calendar.
Edge & IoT integration
Ingest from your sensors, SCADA, and historians.
Explainable alerts
Every alert shows why, so technicians trust and act on it.
CMMS integration
Push work orders into the systems your team already uses.
How we'd build it
The same method behind everything we ship: de-risked, measured, and in production in one quarter.
Feasibility
We validate the use case on your real data, define the accuracy bar and ROI, and give you an honest go/no-go — usually in weeks.
Build & evaluate
We engineer the system and score it against a labelled set until it clears the bar — quality is a measured number, not a promise.
Deploy
We ship to production, integrated with your systems and behind the right human-approval gates, with monitoring from day one.
Operate & improve
We watch it in production, handle drift, and expand scope as trust builds — feasibility in 6 weeks, production in ~90 days.
What you get
- Prioritized, explainable failure predictions
- Less unplanned downtime and fewer wasted part swaps
- Alerts wired into your maintenance workflow
- Monitoring that adapts as equipment and conditions change
Production AI we've shipped
We'd build your solution with the same discipline behind these real, in-production engagements — described by sector under NDA.
A production document classifier — 5,000+ documents/month, built to a ≥95% accuracy target and live in 8 weeks.
Read the case studyBank-statement automation across 21 banks with a measured 0.24% transaction-direction error rate.
Read the case studyAI-at-POS recommendations running against live in-store transactions across every location.
Read the case studyWant to scope Predictive Maintenance?
Tell us the use case. We'll come back with a feasibility view and an honest go/no-go — a working prototype in about six weeks.
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Location
Barcelona, Spain • Lisbon, Portugal • Sheridan, WY, USA (incorporation)
Why Choose CONE RED?
- AI R&D lab & venture builder — production systems, not slideware
- Feasibility in 6 weeks, production in ~90 days
- Accuracy measured and evaluated — never invented or guaranteed
- Real engagements across financial services, healthcare & retail
- End-to-end support from strategy to deployment
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