Build & Scale Smart Factory Solutions
with Industrial Data & AI

Enterprise Data Intelligence

Strategic Intelligence Layer: Business Context

Synthesises operational data from multiple sites with corporate business systems to enable strategic decision-making through business analytics, forecasting, and compliance reporting.

Receiving contextualized operational insights, it integrates manufacturing performance with financial, supply chain, customer, and market data to provide executive visibility, cross-site benchmarking, and enterprise-wide optimization opportunities.

This layer feeds advanced use cases, Real-Time Analytics, Digital Twins, AI/ML Optimization, and Agentic AI, that require both operational depth and business breadth, enabling data-driven strategy from shop floor to executive suite.

Operational Data Intelligence

Operational Intelligence Layer: Operational Context

Establishes a unified, governed framework that integrates diverse operational and enterprise data sources into a consistent digital representation of your operations.

By collecting, contextualizing, and transforming data with rigorous governance controls, it creates a common operational context where both human decision-makers and intelligent automation systems can reliably access, interpret, and act on accurate, real-time information.

This layer feeds both local decision-making and upstream Enterprise Data Intelligence while maintaining consistency across multiple manufacturing sites through standardized data models and governance frameworks.
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Unified Namespace

Real-Time Layer: Live Operational Context

Creates a centralized, event-driven data architecture that provides structured access to all operational data and information as it flows in real-time.

By establishing a semantically organized information backbone, where every asset, process, sensor, and business event publishes its state continuously, it eliminates point-to-point integrations and creates a living digital representation of operations.

This layer enables digital systems to publish and subscribe to contextualised operational data, triggering intelligent orchestration, real-time analytics, and immediate response to changing production conditions across the entire operational landscape.

Industrial DataOps

Enablement Layer: Systematic Interoperability

Establishes a systematic and repeatable methodology for addressing the disconnect between how operational data is produced (equipment protocols, system formats, domain-specific structures) and how it needs to be consumed (analytics models, business applications, visualization tools).

It is architecturally neutral, with emphasis on reusability, observability, and maintainability. Through  no/low-code tooling, it empowers OT and IT users to participate in formal data engineering workflows, enabling cross-functional teams to collaboratively engineer data interoperability at scale.

This enables organizations to rapidly scale their digital operations, maintain data quality across hundreds or thousands of assets.

Digital Connectivity

Foundational Layer: Source Integration

Establishes the foundational integration layer that connects diverse operational systems to the digital intelligence platform using open and standardized connectivity protocols and information modeling.

By deploying infrastructure that leverages technologies like OPC UA for equipment-level semantic interoperability, MQTT for efficient edge-to-cloud communication, and Kafka for enterprise-scale event streaming, it creates a protocol-appropriate pipeline.

Adhering to information modeling standards like  ISA-95, OPC UA, AAS and DTDL, ensures that extracted data carries semantic meaning and structural relationships, enabling upstream systems to understand  operational context without custom interpretation logic.

Data Historisation

Persistence Layer: Historical Intelligence

Provides robust, high-performance storage and retrieval of time-series and event-based operational data, creating a permanent record of manufacturing operations for analysis, compliance, and continuous improvement.

By capturing and preserving operational metrics, process parameters, equipment states, and discrete events with precise timestamps and full fidelity, it transforms real-time operational data into queryable historical intelligence.

This enables trend analysis, root cause investigation, performance benchmarking, predictive maintenance, regulatory compliance, and data-driven optimization based on weeks, months, or years of operational history.