Asset Intelligence — predicting failures before they take production offline.
Predictive operations for the asset-heavy industries that run on uptime — generation, transmission, distribution, oil and gas, water utilities. Sensor telemetry combined with maintenance history and operational context to predict failures, schedule interventions, and shift maintenance from calendar-based to condition-based.
Asset failure predicted from sensor telemetry, not detected after
Maintenance triggered by asset condition, not the calendar
Predictions wired into work-order, dispatch, and crew workflows
Every prediction, decision, and intervention logged
01 / THE CHALLENGE
Asset-heavy industries running on calendar-based maintenance — when sensor data could predict the failure.
Energy and utility operators run thousands of critical assets — turbines, transformers, pumps, pipelines, distribution gear — most of them maintained on schedules tuned years ago, monitored on dashboards no one watches in time, and serviced through work orders that move slower than the failure mode.
The cost of reactive maintenance shows up as unplanned outages, SLA penalties, regulatory exposure, and the operational margin lost when a critical asset fails between maintenance windows. The sensor data is there. The maintenance history is there. The operational machinery to combine them and act in time is not. The traditional response — more inspections, more dashboards, more maintenance crews — addresses volume but not predictive capability. Asset Intelligence puts machine learning across the sensor stream and historical maintenance data, predicts failure modes before they take assets offline, and wires predictions into the work-order and field dispatch systems where action actually happens.
02 / THE APPROACH
Four phases. Each one ships agent capability into citizen channels.
CODE81 delivers the Citizen Service Agent in four phases — designed so the agent is in production handling real citizen traffic by the end of the second phase, not at the end of a 12-month transformation programme.
- Asset audit & data inventory — Audit critical asset classes, sensor coverage, and historical maintenance data. Prioritise the asset categories with highest unplanned-failure cost or regulatory exposure. Design the predictive model architecture and the integration map with EAM and field systems.
- Predictive models & first deployment — Build the first wave of predictive models — anomaly detection, remaining useful life, failure mode classification. Productionise into the maintenance workflow for one priority asset class. Wire predictions into work orders and crew dispatch.
- Asset coverage & condition-based maintenance — Extend predictive coverage across additional asset classes. Move maintenance schedules from calendar-based to condition-based on the assets where it makes sense. Optimise crew dispatch on predicted impact, not ticket order.
- MLOps, retraining & capability transfer — Lock in MLOps — drift monitoring, scheduled retraining, end-to-end outcome measurement against availability, MTBF, and unplanned outage cost. Transfer ownership to the operator's reliability engineering and operations teams.
03 / THE SOLUTION
Six components that make up a production-grade Asset Intelligence & Predictive Operations.
The full reference architecture — what gets built, how the pieces fit together, and where the governance controls sit.
/ COMPONENT 01
Sensor Telemetry Ingestion
Real-time ingestion of asset sensor data — vibration, temperature, pressure, current, flow — the foundation every predictive model and operations workflow reads from.
/ COMPONENT 02
Predictive Failure Models
Machine learning models that predict asset failure modes from sensor signals — tuned to the operator's asset estate and historical failure patterns.
/ COMPONENT 03
Remaining Useful Life Engine
Continuous remaining useful life scoring on critical assets — supporting condition-based maintenance and capital planning decisions.
/ COMPONENT 04
EAM & Work Order Integration
Predictive output wired into the enterprise asset management and work order systems — interventions scheduled and dispatched on prediction, not breakdown.
/ COMPONENT 05
Crew Dispatch & Routing
Field crews routed on predicted impact and asset criticality — high-impact predictions take precedence over ticket queue order.
/ COMPONENT 06
MLOps & Outcome Measurement
Drift monitoring, scheduled retraining, and outcome measurement against availability, MTBF, and unplanned outage cost.
Sense
Real-time sensor telemetry across critical assets ingested continuously.
Reason
Predictive models score failure risk, anomaly likelihood, and remaining useful life.
Decide
Reliability and operations teams receive structured predictions with criticality and intervention context.
Act
Work orders generated, crews dispatched, interventions scheduled — every action logged and outcome measured.
What citizen service leaders fund this for.
Industry benchmarks across the categories CODE81 delivers for public-sector clients. Sourced from analyst firms and sector research — not internal estimates.
Reduction in unplanned downtime when predictive maintenance replaces calendar-based schedules
Lower maintenance cost through condition-based scheduling versus fixed maintenance windows
Asset condition monitored continuously versus quarterly inspection cycles
05 / TECHNOLOGY
Built on enterprise AI platforms with public-sector data residency.
Reference architecture — the platforms and integration patterns CODE81 uses to deliver the Citizen Service Agent. Specific platform choices tuned to each client's existing estate and regulatory context.
AI & Predictive Operations
Asset & Operations Integration
Data & Governance
/ Engagement Disclosure
This is a forward-looking use case CODE81 designs and delivers for government and public-sector clients across the region. Live engagement details, reference architectures, and customer references are available under NDA on request.
Have asset operations
still running on calendar-based maintenance?
We've built predictive operations capability for energy and utility operators across the region — moving reliability and field teams from reactive maintenance to proactive intelligence. Send us the use case and we'll respond with the architecture, governance shape, and a 30-minute scoping call — usually within the same business day.
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