MaestroHub is an Industrial Context Platform that turns fragmented factory signals into structured, AI-ready operational context. It connects PLCs, SCADA, MES, ERP, and sensors, standardizing their data into one ISA-95 Unified Namespace (UNS) — a real-time, governed view people, BI, and AI can act on. Beyond moving data, it runs the full loop (connect → govern → store → visualize → decide → act → learn) as a single lightweight Go binary that scales to Kubernetes with zero-ops.

Most manufacturers run on fragmented systems and inconsistent data, leaving 20–30% untapped performance, 70%+ unused data, and stalled AI projects — each site's tags and KPIs turn every rollout into a new integration project. MaestroHub creates a unified, governed operational context layer across machines, MES, PLCs, and ERP — without rip-and-replace. Best fit: multi-site manufacturers (automotive, appliances, chemicals, metals, F&B) needing real-time data to lift OEE and enable AI.

Yes. MaestroHub creates a governed foundation for industrial AI by unifying operational and business data while preserving its context, schemas, relationships, transformations, and lineage. Through MCP, compatible AI agents can securely access authorised data and functions, with granular controls over permitted sites, assets, data, and actions.

MaestroHub is an industrial DataOps and Unified Namespace platform—not simply an MQTT broker, SCADA replacement, integration tool, or analytics application. It complements existing systems by adding connectivity, data modelling, orchestration, governance, lineage, access control, data-health monitoring, and operational intelligence across the architecture.

No. MaestroHub works with existing SCADA, MES, historian, ERP, edge, and cloud platforms through interfaces such as OPC UA, MQTT, REST APIs, databases, and files. These systems can remain operational systems of record, while MaestroHub provides a shared data layer that models, contextualises, orchestrates, and traces data for reuse across the architecture.