Core capabilities
01 | Understand enterprise data so AI can read the business
Connect databases, business systems, files, and real-time data. With AI-assisted understanding and business calibration, turn scattered data into information that can be related and used.
02 | Accumulate reusable intelligence for data governance
Package data processing, algorithms, and business rules as composable capabilities the enterprise can keep reusing.
03 | Build a business ontology so AI understands the enterprise
Model entities, relations, events, and rules so enterprise data becomes a business context AI can use for complex analysis and decisions.
04 | Build enterprise agents that take part in execution
Agents call data, tools, and processes in business context to analyze, judge, and execute tasks under permission control and tracing.
05 | Build agents quickly so industry apps can expand
Start from a business goal and compose ontology, Skills, and process capabilities into industry applications for different scenarios.
06 | Connect analysis to action so insight becomes value
Combine analytics, risk judgment, and processes so discovery, decision support, and execution form a closed loop.
FAQ
Which manufacturing scenarios fit production operations intelligence?
- Operations that need analysis across ERP, MES, PLM, QMS, equipment, and quality data—such as exception localization, quality traceability, equipment ops, and delivery coordination.
Do we have to replace existing production systems?
- No. The approach connects existing databases, business systems, and real-time data to build a related business view on top, rather than replacing shop-floor systems all at once.
How does it enter a business closed loop after go-live?
- The system connects discovery, root-cause analysis, and processes so results can enter handling, tracking, and continuous improvement instead of stopping at reports.