Connecting Digital Twins, Operational Systems, Enterprise Knowledge, and AI into a Unified Decision Intelligence Layer.
Your EAM, SCADA, Historian, and Digital Twin platforms already provide operational data. AssetIQ extends beyond them — adding the intelligence layer that transforms data into context, context into decisions, and decisions into coordinated actions across your enterprise.
Industrial enterprises face a common set of operational challenges that fragmented tools and disconnected systems were not built to solve.
Unexpected equipment failures remain one of the largest sources of operational loss across industrial enterprises. As systems become more interconnected, downtime impacts production, visibility, compliance, and business performance simultaneously.
Maintenance can represent a significant share of operating expenditure in asset-intensive industries. Fixed schedules often fail to reflect actual equipment condition, resulting in unnecessary activity, avoidable downtime, or delayed intervention.
Industrial organizations generate massive amounts of operational data, yet much of it remains fragmented across historians, SCADA, MES, EAM, ERP, and Digital Twin systems. Critical insights often fail to reach the teams responsible for operational decisions and execution.
Industrial organizations are increasingly dependent on interconnected OT and IT environments. Recovery speed has become a critical business metric — downtime costs escalate exponentially with every hour of disruption, directly impacting revenue and customer commitments.
Industry benchmarks across predictive maintenance, asset performance management, and operational intelligence deployments.
Predictive intelligence enables earlier intervention by identifying operational risks before failures occur, reducing unexpected disruptions and improving operational continuity.
Condition-based and predictive maintenance strategies reduce unnecessary inspections, optimize maintenance schedules, and improve resource utilization across the enterprise.
Operational intelligence helps organizations maximize equipment uptime and production capacity through proactive risk detection and coordinated operational decisions.
Most platforms stop at visibility or representation. AssetIQ operates at Level 5 — the intelligence layer that transforms data into decisions and actions.
Asset registry, work orders, maintenance records, spare parts.
Condition monitoring, health scores, reliability analytics, RUL prediction.
3D visualization, real-time IoT overlay, historical playback, scenario simulation.
AI-powered analytics, knowledge graphs, root cause analysis, cross-system correlation, and decision recommendations.
Embedded AI reasoning, predictive & prescriptive intelligence, automated workflow orchestration, continuous learning.
AssetIQ extends beyond traditional Digital Twin and APM platforms by adding the Decision Intelligence layer — connecting operational data, enterprise knowledge, AI reasoning, and business workflows into a unified decision-making platform. Your existing EAM, SCADA, Historian, and Digital Twin investments are enhanced, not replaced.
The result: your organization moves from asking "what is happening?" to automatically knowing what will happen, why it will happen, and what to do about it — with AI agents that execute across systems.
Each capability is powered by AI at its core — not a collection of point solutions bolted together.
Not a static 3D model — a live, sensor-driven replica that updates every second. Machine status, alarm beacons, OEE, energy trends, and production line health are overlaid directly on the twin and streamed via WebSocket in real time.
Risk scores (0–100) and Remaining Useful Life predictions for every asset, backed by SHAP explainability. Multi-model support — XGBoost, LSTM, Random Forest, Isolation Forest — with automated model drift detection and retraining.
The AI Copilot is the interface to the twin — operators ask questions in natural language instead of navigating dashboards. Grounded in real-time sensor data, maintenance records, and SOPs, it explains alarms, diagnoses root causes, and retrieves procedures instantly.
Maintenance work orders are generated automatically from AI risk predictions — not manual threshold rules. Full work order lifecycle from DRAFT to CLOSED across Preventive, Predictive, Corrective, and Emergency types, with digital LOTO and Permit to Work for HSE compliance.
Combine AI predictions with what-if simulation — ask "what if this AI-flagged risk becomes a failure?" before committing resources. A visual process simulator with drag-and-drop canvas, real-time state propagation, and multi-constraint production optimization.
AssetIQ is the only platform covering Energy, Utilities, and Manufacturing under a single deployment.
Most industrial platforms stop at visibility. AssetIQ closes the gap between visibility and execution — transforming Data into Context, Context into Intelligence, Intelligence into Decision, and Decision into Action.
AssetIQ extends beyond traditional Digital Twin and APM platforms. Your EAM, Historian, SCADA, and Digital Twin investments stay in place and are enhanced — not replaced. AssetIQ adds the Decision Intelligence layer on top, connecting and reasoning across all of them.
Every risk score includes SHAP-based feature attribution so engineers understand why a machine is flagged at risk. No black-box predictions — operators make confident decisions backed by evidence.
What-if simulation is a standard operational tool, not a specialist feature. Test the impact of a maintenance window, equipment failure, or demand surge on your production schedule before committing resources.
Automated model drift detection monitors feature and prediction drift continuously. When a model degrades, retraining is triggered automatically — keeping AI accuracy aligned with real-world conditions without manual intervention.
AssetIQ enables organizations to evolve: from reactive operations (responding after failure) to predictive operations (anticipating failures) to intelligent operations (AI agents that proactively orchestrate decisions and actions across assets, plants, and business functions).
Start with a Discovery Workshop — we map your pain points, data sources, and priority use cases in one day. From there, a tailored proof of concept on your real data typically takes 4–6 weeks.