Asset performance:
known, predicted, optimised.
The Asset Intelligence App Template provides the scaffolding for six core AI capabilities. Systems integrators with depth in asset performance management build the customer's solution on it – configuring the sector vertical, modelling the customer's specific assets and failure modes, and integrating with the customer's telemetry and maintenance systems.
Asset condition is the foundation of everything operational. Yet most enterprises operate with fragmented condition pictures – DCIM here, SCADA there, CMMS somewhere else, inspection records in PDFs, maintenance history in spreadsheets. No single system can answer the question that matters: which asset, given its current condition and what depends on it, is most likely to fail next, and what is the highest-value intervention timed to which window?
Six AI capabilities are scaffolded in the App Template.
The Asset Intelligence App Template provides the scaffolding for six core AI capabilities. The reasoning patterns, semantic structures, and integration points that an implementation needs.
Systems integrators with depth in asset performance management take the scaffolding and build the customer's solution on it: configuring the sector vertical, modelling the customer's specific assets and failure modes, integrating with the customer's telemetry and maintenance systems, and tuning the reasoning to the customer's operational context. What the template gives is the foundation; what makes the deployed app valuable is the integrator's asset-performance domain expertise applied on top.
Consequence-weighted failure prediction
Forecast failures on instrumented assets before they occur, grounded in multi-signal pattern recognition across telemetry, maintenance history, and operating context. The semantic knowledge graph connects each asset to the assets around it and to the systems it serves, so predictions respect the relationships that determine whether a failure matters. Not just the condition of the asset in isolation. The consequence-weighted ranking of which failure to act on first is where Asset Intelligence hands off to Operations Intelligence.
Anomaly detection & condition monitoring
Monitor complex multi-signal patterns: vibration, temperature, pressure, current – to detect abnormalities and identify failure modes that threshold-based monitoring cannot see. Surface only what warrants engineering attention; reduce the noise floor.
Improve Energy efficiency
Analyse power consumption patterns to identify inefficiencies: standby waste, idle running, simultaneous heating and cooling, manually overridden setpoints, sub-optimal scheduling. Quantify the cost; close the loop into operations.
Asset health scoring
Score the health of every monitored asset from condition trends, anomaly history, and operating context. A configurable scoring framework that the systems integrator tunes to your asset classes and operating regime, with visualisation patterns that surface estate-wide health at a glance for engineering and maintenance leadership.
Performance benchmarking against historical baselines
Benchmark each asset and system against its own historical baseline: the asset drifting from its commissioned profile, the system operating outside its own efficient envelope. Sets the foundation for cross-portfolio peer benchmarking where data scale and integrator capability support it.
Risk-based maintenance scheduling
Rank asset importance by failure probability × consequence. Direct maintenance resource to the equipment that carries the highest integrated risk and not the equipment whose run hours are highest. Maintenance spend goes where it buys most outcome.
Closed-Loop Actions.
Intelligence that doesn't change work is just a dashboard. The Asset Intelligence solution closes the loop. Prioritised, evidence-backed interventions flowing into the systems your maintenance team already runs, auditable end-to-end. The Asset Intelligence App Template scaffolds the closed-loop patterns; your systems integrator builds them into your specific CMMS, EAM, and contractor framework on Twinit's composable platform.
Prioritised work, not alerts
Ranked interventions reach your CMMS or EAM with the diagnosis attached: which asset, what condition trend, what predicted timeline, what evidence backs the call. Your maintenance team receives work to do in their own workflow, not anomaly alerts in yet another dashboard. The template scaffolds the pattern; the SI builds the integration into your specific system.
Every recommendation, traceable
The technician sees the suspected failure mode, what the data shows, and the predicted timeline. The maintenance manager sees the ranking rationale. Every recommendation traces back to the data points reasoned over. Provenance by design. Twinit's semantic graph and AI orchestration make this traceability possible; the SI implements the work-order patterns your team will trust.
Hold vendors to the data
Track service contractor and OEM-MARC performance against the same condition data: before service, immediately after, and thirty days on. Did the intervention actually move asset health? The solution makes it visible. The SI builds the contract-management views your procurement and engineering teams need, on the platform's reasoning foundation.
The six capabilities, across any sector.
The six capabilities scaffold identically across all supported sectors. What changes is the asset reality: an AHU in a building, a transformer in a substation, a CNC machine on a production line, a haul truck on a mine plan. All sector. One template architecture.
