ServitizeIQ · AI Predictive Maintenance
Your equipment signals failure days before it happens. Is your service team listening?
ServitizeIQ monitors asset condition continuously and surfaces equipment failure risk 8 to 11 days before breakdown. Your service team acts on a structured alert from the AI prediction engine, not a call from the customer reporting the problem.
Machine health signal, live
8 to 11 days of advance warning delivered before the equipment ever stops.
Bearing wear detectedSeverity: High
Failure preventedService scheduled, no downtime
8–11d
Average advance warning before a failure event
40%
Fewer unplanned field callouts through condition-based maintenance
3×
Earlier failure detection vs. scheduled preventive inspection
8wk
From integration to first AI failure detection alert
The moment the call arrives, you are already late
Know Before the Customer Calls
When a machine stops at a customer site, the call arrives at your service desk. By then the equipment has failed, the customer has lost production, and your engineers are responding to damage rather than preventing it. The gap between when a machine begins degrading and when your team learns about it is the window reactive maintenance cannot close, and it’s the window condition-based maintenance is built to close.
ServitizeIQ closes that window through continuous machine health monitoring. The AI engine analyses vibration, temperature, pressure, and cycle data against equipment-specific failure signatures. When an asset trends toward failure, a severity-ranked alert reaches your service team, typically 8 to 11 days before any symptom appears on the customer’s floor. Your engineers act on predictive intelligence. The customer never experiences the failure event.
Critical Alerts Console: production-affecting faults only, severity-ranked by the AI prediction engine, auto-refreshed every 5 minutes. Warning noise filtered out.
Failure knowledge trapped in engineer experience
Stop Losing Failure Knowledge
Most equipment manufacturers have no structured library of how their machines fail. The specific sensor patterns that precede a bearing fault, a seal failure, or a motor overload exist only in the experience of senior service engineers. When those engineers are unavailable or move on, the next technician starts from zero. The same failure mode recurs, and the cost of that knowledge gap compounds across every deployed asset in the portfolio.
The ServitizeIQ Failure Library captures and operationalises that institutional knowledge across every connected asset. Failure modes, fault trees, and sensor thresholds are structured once by your service team and applied automatically across the entire installed base. AI failure detection alerts are calibrated to your equipment’s specific failure signatures, not to generic industry averages that miss the nuances of how your machines actually degrade.
Failure Library: 48 structured failure modes across mechanical, electrical, hydraulic, and pneumatic categories. Applied automatically across every connected asset.
Managing 8 customer accounts with no consolidated picture
One Dashboard, Every Account
A service engineer assigned to 8 customer accounts has no consolidated starting point for the day. They open each account individually, review recent activity, look for anomalies, and move to the next. If an asset at account 6 was degrading overnight, they find out when they get there, or when the customer calls first.
The My Accounts dashboard transforms the first two minutes of every service engineer’s day. RAG-sorted account cards surface exactly which customer sites need attention without opening a single account. Seven unread alerts across 8 customer tenants are visible at a glance. Condition-based maintenance decisions start from a complete picture, not a manual review.
My Accounts Dashboard: 8 customer accounts, RAG-sorted by machine health risk. 7 open alerts surfaced without opening a single account.
“
The new operational baseline
Predictive maintenance has moved from advanced capability to industry standard – reactive service models are increasingly being measured against it, and found inadequate.
— McKinsey & Company, Rewiring Maintenance with Gen AI, February 2025
90%
Average advance warning before a failure event
84%
Fewer unplanned field callouts through condition-based maintenance
1.4T
From integration to first AI failure detection alert
How leading OEMs apply AI predictive maintenance
Proven Across Leading OEMs
Manufacturers who built condition-based maintenance early now compete on service terms their reactive rivals cannot match.
Caterpillar
Cat Connect · 1.4M assets monitored
Caterpillar’s AI predictive maintenance system monitors over 1.4 million deployed assets globally. Real-time telemetry from each connected machine, covering operating hours, fault codes, and component condition, feeds an anomaly detection layer that surfaces failure risk before customers experience downtime. For a manufacturer whose service agreements carry uptime commitments, this machine health monitoring capability is not optional infrastructure. It is the operational foundation that makes those commitments commercially viable.
BMW Group
AIoT Predictive Maintenance · 2025 to 2026
BMW’s production equipment now uses AI-driven vibration analysis to predict component failures 8 hours before they occur, protecting production output and avoiding unplanned line stoppages. The same principle that governs BMW’s internal failure detection strategy governs the OEM service challenge: the asset is generating the signal that predicts its own failure. The question is whether your maintenance intelligence platform is structured to receive and act on it.
Connected Pump Monitoring · Industrial process equipment
Flowserve
Flowserve deployed IoT-enabled condition monitoring across its pump installed base in markets where equipment availability directly affects customer output. Continuous monitoring surfaced early degradation signals that fed directly into service scheduling, translating into stronger renewal orders and improved asset availability across the deployed portfolio. For Flowserve, connected equipment monitoring was not a technology project. It was the commercial decision that changed what kind of service contracts they could offer and win.
Frequently asked questions
Common questions from leadership teams.
Scheduled preventive maintenance runs on a fixed calendar: every 3 months, every 500 hours, regardless of the equipment's actual condition. It services assets that do not need it and misses assets that do. AI predictive maintenance instead analyses continuous sensor data to identify the specific patterns that precede failure for each asset class. ServitizeIQ's condition-based maintenance approach means interventions are triggered by machine health signals, not by calendar dates. The result is fewer unnecessary service visits, fewer missed failure events, and a 40% reduction in unplanned field callouts for OEMs who have made the transition.
ServitizeIQ integrates with your deployed equipment via MQTT, REST APIs, OPC-UA, and hardware gateways, using the protocols already present at customer sites. No hardware replacement is required at any customer installation. For equipment manufacturers with existing IoT infrastructure, the first assets are typically live and generating condition data within 8 weeks of integration start. For manufacturers deploying connectivity for the first time, gateway provisioning runs in parallel with platform setup so the timeline stays consistent.
Accuracy depends on two factors: the quality and frequency of sensor data, and how well failure models are calibrated to specific equipment types. ServitizeIQ addresses both. The Failure Library lets OEM service teams encode the specific failure signatures of their equipment, the sensor patterns that precede bearing wear, seal degradation, or motor thermal stress, rather than relying on generic industry models. Once calibrated to an asset class, the prediction engine consistently delivers 8 to 11 days of advance warning before failure events. ServitizeIQ's models are trained on equipment-specific failure data, not on generalised industrial datasets.
Yes. ServitizeIQ is designed to complement existing operational systems, not replace them. Asset health data, condition-based maintenance alerts, and work order outputs are exportable via API to your ERP, CRM, and field service management platforms. The AI predictive maintenance intelligence generated by the platform is available across service, commercial, and finance teams, not locked inside a standalone tool. For manufacturers using platforms such as SAP, Salesforce, or ServiceNow, integration paths are established during onboarding.
It depends on the sensor baseline already present at the asset. Most industrial equipment manufactured in the last 15 years includes vibration monitors, temperature sensors, and pressure transducers as standard, which is sufficient for ServitizeIQ's condition monitoring to generate predictive signals. For genuinely legacy assets with no existing sensors, ServitizeIQ's hardware gateway partners support low-cost retrofits that install without machine downtime. The platform is built for OEMs managing a mixed portfolio of equipment generations, not only manufacturers with recently commissioned smart assets.
Outcome-based contracts, where the OEM guarantees equipment availability rather than selling service time, require two things reactive maintenance cannot provide: the ability to prevent failures before they occur, and the structured data to prove that failures were prevented. ServitizeIQ addresses both. Condition-based maintenance interventions prevent the failure events that would breach an SLA commitment, and the platform's timestamped alert and intervention records provide the evidence that availability targets were met. Most manufacturers using ServitizeIQ reach their first outcome-based service contract within 90 days of connecting assets.
See It In Action On Your Own Equipment.
The platform demonstration starts with your asset types, deployment geography, and service model, not a generic product walkthrough. We’ll show you what AI predictive maintenance changes for your specific service operation.
No hardware replacement required · Connects to existing IoT infrastructure · First AI failure detection alert within 8 weeks