Microsoft Azure provides the cloud infrastructure, analytics, AI services, and data platform. DeepIQ complements Azure by connecting operational technology (OT), engineering technology (ET), and enterprise IT into a unified contextual intelligence layer.

Rather than simply moving industrial data into Azure, DeepIQ gives it meaning by connecting assets, engineering relationships, operational history, and business context—making analytics and AI significantly more valuable.

No.

DeepIQ is designed to enhance your Microsoft data platform, not replace it.

Microsoft Fabric, OneLake, and Azure Databricks provide powerful analytics and data management capabilities. DeepIQ enriches those platforms with industrial connectivity, engineering intelligence, business context, and reusable industrial data products.

Together they create a trusted foundation for industrial AI.

DeepIQ securely connects industrial systems—including historians, SCADA, PLCs, engineering systems, maintenance applications, and ERP platforms—to your Azure environment.

It can then:

• Ingest and transform industrial data
• Create reusable data products
• Build business context
• Connect engineering drawings to live operational data
• Deliver contextual intelligence to Azure analytics and AI services

Organizations continue using their existing Azure architecture while DeepIQ provides the industrial intelligence layer.

No.

DeepIQ is designed around customer-owned data.

Organizations maintain control over where their data resides while DeepIQ provides connectivity, contextualization, orchestration, and governance.

This approach allows customers to leverage Azure without creating another proprietary industrial data repository.

DeepIQ complements many Microsoft technologies, including:

• Microsoft Azure
• Microsoft Fabric
• OneLake
• Azure Databricks
• Azure Data Lake Storage
• Azure Kubernetes Service (AKS)
• Azure AI
• Azure Machine Learning
• Microsoft Copilot
• Microsoft Power BI
• Microsoft Entra ID
• Microsoft Defender for Cloud

DeepIQ integrates into your existing Microsoft ecosystem rather than replacing it.

Yes.

DeepIQ was designed specifically for industrial environments and supports connectivity to:

• AVEVA PI System
• AspenTech IP.21
• OPC UA
• PLCs
• SCADA systems
• DCS platforms
• SQL databases
• CMMS
• ERP systems
• Engineering databases
• Laboratory systems

This allows organizations to modernize without replacing the systems that run their operations.

Large language models understand language—but they don't automatically understand industrial operations.

DeepIQ grounds Microsoft AI experiences in trusted operational and engineering context by connecting:

• Assets
• Equipment
• P&IDs
• Engineering documentation
• Historian data
• Maintenance records
• Operational events
• Business systems

This enables copilots and AI applications to generate responses that are more accurate, explainable, and relevant to industrial operations.

Yes.

DeepIQ helps organizations migrate historical and real-time operational data into Azure while preserving critical engineering context.

This includes:

• Asset hierarchies
• Metadata
• Tag relationships
• Time-series quality
• Engineering relationships
• Data lineage
• Provenance

Rather than simply moving historian values, DeepIQ preserves the operational meaning behind the data.

Industrial Data Products are reusable, governed datasets that combine operational data, engineering logic, business rules, metadata, and governance into a standardized asset.

Instead of building one-off integrations for every project, organizations create trusted data products that can be reused across analytics, reporting, AI, and operational applications.

Yes.

DeepIQ PIDx uses AI to convert P&IDs and engineering drawings into structured, searchable engineering intelligence.

The extracted equipment, instrumentation, process relationships, and engineering metadata can then be connected directly to operational and enterprise data within Azure.

This enables applications such as:

• Process Safety Management
• Management of Change (MOC)
• Digital twins
• Engineering search
• Asset integrity
• AI assistants