连接与治理
接通你的系统、政策与知识，让 AI 在你的规则之内工作，而不是绕过它们。
/ Operations & Automation /
Fix it before it breaks. Not after.
AI that monitors equipment health, detects failure signals and recommends maintenance actions — so unplanned downtime becomes a managed exception, not a recurring crisis.
The problem
Unplanned downtime derails operations without warning
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Unplanned downtime that derails operations
Equipment failures happen without warning. The cost isn't just repair — it's lost production, missed SLAs and emergency contractor rates.
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Scheduled maintenance that wastes resource
Fixed-interval maintenance replaces parts that are still functional and misses components that are actually degrading.
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Sensor data collected but never acted on
Equipment generates enormous volumes of operational data. Without AI to interpret it, that data sits unused while failures develop.
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Plans disconnected from operations
Maintenance schedules ignore production priorities and asset criticality. Downtime shifts rather than shrinks.
How it works
从信号到成果——治理内置
自动化与辅助
让工作流上线：分流、起草、解决或升级，全程带完整上下文与审计追溯。
度量与改进
跟踪运营 KPI、质量与风险——再和你的团队一起调优剧本。
流程序列会适配你的工具、渠道与风险态势。
Fix it before it breaks. Not after.
What's included
What you get when you run this with Thinkia
A governed layer across data, workflows, and handoffs—so teams ship safely and scale with metrics.
Sensor data integration
Connects to IoT sensors, SCADA systems and operational data sources across your asset base.
Failure prediction models
Detects early signs of component degradation before failure occurs.
Maintenance recommendation engine
Suggests the right maintenance action, at the right time, for the right asset.
Asset health dashboard
Real-time view of equipment status, risk scores and upcoming maintenance priorities.
Work order automation
Triggers maintenance work orders in your CMMS when thresholds are crossed.
Failure pattern library
Builds an organisation-specific library of failure signatures that improves prediction accuracy over time.
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Results
What changes when this runs in production
Results vary by asset type, sensor coverage and operational environment.
–40%
Reduction in unplanned equipment failures after deployment
Orientative — confirmed in discovery; depends on the starting point.
–25%
Reduction from eliminating unnecessary scheduled interventions
Orientative — confirmed in discovery; depends on the starting point.
+30%
Improvement in asset reliability with condition-based maintenance
Orientative — confirmed in discovery; depends on the starting point.
How we work
From calendar maintenance to signals that prevent unplanned stops
Asset criticality
Asset criticality
Week 1–2
Lines, sensors, and failure modes are ranked by downtime cost and data availability.
Model & alert
Model & alert
Week 3–5
Thresholds, lead times, and work-order integration are tuned with maintenance planners.
Field pilot
Field pilot
Week 6–9
Technicians validate alerts; false positives are reduced before broadening asset classes.
Fleet scale
Fleet scale
Week 10+
Spares, crews, and schedules align to predicted demand; continuous learning respects safety gates.
Sensor coverage and industrial network policies set pace; we start where telemetry is trustworthy.
Go deeper
Reliability, prediction, and proactive AI
Ideas, trends, and tools to stay ahead
Get started
Ready to scope this for your context?
We start with a focused session—no commitment—to map constraints and a sensible path.