DeepIQ Word Plug-In (CoPilot): Industrial DataOps Assistant for Technical Documentation

The DeepIQ Word Plug-In, known as CoPilot, is a domain-specific AI assistant purpose-built for drilling and well engineering teams.  Integrated directly into Microsoft Word, CoPilot connects users to DeepIQ’s industrial knowledge graph and Databricks-native data pipelines, automating the generation of high-quality technical documentation, including drilling data, offset analyses, risk assessments, and post-well reviews.

Unlike general-purpose AI tools, CoPilot is powered by curated, structured data.  It leverages offset well analytics, structured time series, historical drilling reports, and best practices across regions to produce documents that are technically sound, context-aware, and compliant with engineering standards.

What CoPilot does for drilling teams:

• Surfaces zone-specific offset insights like stuck pipe events, loss circulation zones, and mud weight windows
• Queries DeepIQ’s knowledge graph for similar wells to extract lessons learned, typical hazards, and recommended mitigations
• Auto-populates document sections based on formation targets, casing designs, and operational constraints
• Enables engineers to ask natural-language questions inside Word and receive validated responses from real operational data
• By embedding AI into documentation workflows, CoPilot extends Industrial DataOps to the desktop, linking engineers directly to governed operational data.

How CoPilot works with Databricks:

Enhanced Description of DeepIQ’s Co-pilot Integration with Databricks

DeepIQ has pioneered the persistence of industrial knowledge graphs and asset hierarchies within Delta Lake, not just for drilling, but across domains, providing a foundation for contextualized reasoning, technical automation, and enterprise-scale digital twins natively in the Databricks environment.  Leveraging specialized R&D to achieve a clear separation between schema management and the persistence layer.  This architecture enables scalable, flexible knowledge representation for complex industrial data.

How Co-pilot Leverages Databricks Mosaic Model Serving

DeepIQ’s Co-pilot utilizes Databricks Mosaic Model Serving to deliver advanced automation and intelligence across several critical functions:

• Document Parsing: Automatically extracts and interprets content from technical documents.
• Context Generation: Creates rich, contextual understanding of technical content for more accurate analysis.
• Knowledge Graph Retrieval: Seamlessly accesses and utilizes data from knowledge graphs stored in Delta Lake.
• Content Reformatting: Adapts and restructures information to meet specific documentation standards and requirements.

Seamless User Experience

Co-pilot is fully compatible with Databricks Genie and similar front-end solutions, providing technical teams with a unified, end-to-end workflow, from data exploration to automated documentation.  This integration ensures a smooth, intuitive experience for users working with complex engineering data.

Extensibility Beyond Drilling

While Co-pilot is currently deployed within the drilling domain, its architecture is designed for adaptability.  The solution can be extended to support any technical documentation process that benefits from integrating insights across disparate data sources, making it a versatile tool for a wide range of industrial and engineering applications.

CoPilot on Databricks Genie

CoPilot can be extended into Databricks Genie to deliver an immersive, collaborative experience for drilling and engineering teams directly within the Databricks environment.  By embedding CoPilot’s natural language interface within Genie, users can interact with DeepIQ’s knowledge graph and offset analytics using chat-style queries, retrieve structured content, and generate engineering documentation all within the Databricks workspace.

This integration would enable engineers to explore historical well data, surface relevant lessons learned, and pre-fill drilling program templates in real-time, all within a secure, Databricks-native interface.  Genie would act as the front-end layer, while DeepIQ’s APIs and Delta Lake-backed knowledge models power the data retrieval, contextualization, and document generation behind the scenes.  This creates a seamless, domain-aware workspace for energy professionals to analyze complex data and accelerate high-quality decision-making.

Additional Use Cases on Databricks

DeepIQ’s EDW to Cloud Migration solution, as deployed with Maverick Natural Resources, operates seamlessly with Databricks as the central processing and analytics engine.  By using Databricks as the unified platform for data ingestion, transformation, and warehousing, DeepIQ enables scalable and high-performance workflows that consolidate OT and IT data from production, maintenance, and ESG systems.

DataStudio orchestrates these pipelines across Databricks’ Lakehouse architecture, allowing real-time analytics and automated reporting for cross-functional business domains.  This architecture has delivered a 95% reduction in workflow execution time while dramatically improving visibility across operations, finance, and sustainability initiatives.

DeepIQ’s refinery optimization solution for Global Clean Energy relies on Databricks to unify IT and OT data sources into a single, real-time analytics environment.  Using Databricks’ scalable data lakehouse architecture, DeepIQ integrates diverse systems across the plant, from control systems and sensors to enterprise IT platforms, enabling continuous, high-quality data flows into a centralized environment.

This seamless integration supports near real-time reporting and analysis across operations, maintenance, and business domains.  By orchestrating ingestion and transformation pipelines within Databricks and delivering insights through DeepIQ DataStudio, the solution reduced reporting latency to under one business day, providing 360-degree visibility for faster, data-driven decision-making across the refinery.

Maverick Natural Resources partnered with DeepIQ to optimize lease operator routes using real-time operational data.  By integrating disparate field systems and time series data into the Databricks Lakehouse, DeepIQ enabled dynamic scheduling and automated exception handling.  Field personnel, who previously relied on static, suboptimal schedules across disconnected systems, gained visibility into current field conditions and could respond proactively.  Using AI-driven route generation and exception management workflows,

Maverick achieved a $10M annualized cash flow impact, a 40% reduction in operational issues, and a 15% reduction in field inventory.  This project demonstrated how DeepIQ’s platform, operating natively on Databricks, can automate oilfield operations at scale while delivering measurable operational gains.

DeepIQ partnered with OMV to optimize drilling operations using a Databricks-powered architecture that integrates fragmented operational data into a unified, ML-driven environment.  At the core of the solution is a drilling knowledge graph built on data ingested and transformed through Databricks Delta Lake, enabling scalable analytics and machine learning workflows.

By combining offset well data, real-time drilling parameters, and historical lessons learned, the solution supports advanced simulation and decision-making during well planning and execution.  This integration led to over €4 million in estimated operational gains, driven by faster and more accurate well designs, reduced risk, and significantly lower engineering hours.  Databricks served as the high-performance engine for data processing, modeling, and knowledge retrieval across the entire workflow.

DeepIQ is currently partnering with a $15 billion global petrochemical manufacturer to scale OT/IT contextualization across 26 unique plants and historian systems, utilizing Databricks as the central data and analytics engine.  The solution consolidates diverse sources of time series and enterprise data into a unified platform that enables seamless ingestion, transformation, and analytics.

Built on the Databricks Lakehouse architecture, this deployment supports both real-time and batch processing, enabling the unification of operational and business insights at scale.  By leveraging Databricks for scalable compute and DeepIQ DataStudio for domain-specific analytics and modeling, the customer has significantly enhanced data accessibility and operational visibility.  The project is already delivering measurable improvements in data quality, reporting efficiency, and system-wide analytics across global assets.

DeepIQ collaborated with Databricks on a joint initiative to digitize scanned P&ID diagrams for a $10B natural gas processing company.  The project aimed to extract and contextualize equipment data from complex engineering drawings and align it with real-time OSI PI tag data.  Using DeepIQ DataStudio and Databricks’ scalable Lakehouse platform, the team created a pipeline that interprets image-based P&ID files, identifies key entities, and links them to operational time series data.

This approach achieved 85% accuracy in file-to-tag matching while dramatically improving the speed and scalability of asset contextualization.  Databricks served as the foundation for data persistence, processing, and model execution, enabling the operationalization of insights across plants with consistent accuracy and minimal manual intervention.

To learn more about DeepIQ, explore  our Customer success stories or engage with us through a focused pilot to experience the platform’s capabilities firsthand. Contact us at info@deepiq.com to begin your transformation journey.