Absolutely not.

DeepIQ is designed to extend and enhance your Databricks investment—not replace it.

Databricks provides a powerful platform for data engineering, analytics, machine learning, and AI. DeepIQ adds the industrial context that helps AI understand assets, engineering relationships, operational processes, and business meaning.

Together, they create a trusted foundation for industrial AI.

Databricks excels at organizing and processing data.

Industrial organizations still face challenges such as:

• Connecting OT, IT, and engineering systems
• Reading and contextualizing P&IDs
• Resolving asset identities across multiple systems
• Building industrial knowledge graphs
• Creating reusable industrial data products
• Grounding AI in operational reality

DeepIQ solves these industrial-specific challenges while allowing Databricks to continue serving as your enterprise AI platform.

No.

DeepIQ follows a zero-copy, customer-owned architecture whenever possible.

Your data remains within your preferred cloud environment—whether that's Databricks, Azure, AWS, Google Cloud, Snowflake, or on-premises infrastructure.

DeepIQ adds context and orchestration without creating another proprietary data repository.

Yes.

DeepIQ complements Databricks capabilities by enriching Delta Lake data with engineering and operational context while integrating with enterprise governance practices.

Organizations can continue using Unity Catalog for governance while DeepIQ provides contextual intelligence across industrial assets and systems.

DeepIQ connects to hundreds of industrial and enterprise systems, including:

• OSIsoft PI System
• AspenTech IP.21
• Honeywell Experion
• OPC UA
• MQTT
• WITSML
• PLCs
• SCADA systems
• CMMS
• ERP
• Laboratory systems
• Engineering databases
• Relational databases
• Cloud storage platforms

DeepIQ Reveal securely connects these systems into a unified industrial intelligence layer.

Yes.

DeepIQ Reveal includes secure edge capabilities designed for industrial environments with strict network segmentation and air-gapped architectures.

Data can be processed locally while orchestration and governance remain centralized, allowing organizations to maintain existing cybersecurity policies.

Yes.

DeepIQ supports both real-time streaming and batch processing.

Organizations can ingest historian data, SCADA events, IoT telemetry, engineering information, and enterprise transactions into unified workflows that feed Databricks analytics and AI models.

AI models are only as good as the context they receive.

DeepIQ enriches data with:

• Asset relationships
• Engineering topology
• Equipment hierarchies
• Operational dependencies
• Business context
• Data lineage
• Provenance

This enables copilots and AI agents to generate responses grounded in real operational knowledge, improving trust, reducing hallucinations, and supporting explainable AI.

Yes.

One of DeepIQ's most common use cases is historian modernization.

Organizations use DeepIQ to migrate historical and real-time PI System data into Databricks while preserving engineering relationships, metadata, calculations, and contextual information that are critical for analytics and AI.

Yes.

DeepIQ automatically builds industrial knowledge graphs that connect:

• Equipment
• Sensors
• P&IDs
• Engineering documents
• Operational data
• Maintenance history
• Supply chain information
• Business systems

These knowledge graphs provide the contextual foundation for advanced analytics, enterprise search, GraphRAG, and industrial AI.