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Dinesh Makked
Practitioner ⭐️⭐️⭐️
Dinesh MakkedPractitioner ⭐️⭐️⭐️

Process-Aware Knowledge Graphs for Industrial AIGathering Interest

Inspiration“Context is king in the world of AI.”Across research, publications, and industry discussions, one theme consistently stands out — AI without context lacks true intelligence. To unlock the full potential of Industrial AI, we must ground AI solutions in process context.VisionIntroduce Process-Aware Knowledge Graphs (PAKGs) that integrate process understanding directly into the Cognite Data Fusion (CDF) ecosystem. By capturing and structuring the interconnections, interdependencies, and material flows from Process Flow Diagrams (PFDs), we enable context-driven intelligence for Agentic AI solutions built on Atlas AI and CDF.Core Capabilities System Model Extraction Automatically extract process metadata from P&IDs and PFDs (PDF/Image formats). This removes the dependency on CAD files, which are often unavailable or inconsistent. Process-Aware Knowledge Graph Generation Translate the extracted system model into a Knowledge Graph enriched with process semantics. Represent equipment, process streams, and control loops as nodes and relationships, creating a foundation for process discovery, reasoning, and autonomous insights. Value Proposition Enables Agentic AI systems to reason over process context. Accelerates ROI realization from Cognite solutions by improving AI explainability, traceability, and domain relevance. Lays the groundwork for next-generation Industrial AI applications — from automated root cause analysis to process optimization. AskI propose enhancing CDF to support this capability natively, creating a bridge between engineering documentation and context-aware AI models.

Aditya Kotiyal
MVP ⭐️⭐️⭐️⭐️⭐️
Aditya KotiyalMVP ⭐️⭐️⭐️⭐️⭐️

Cognite VISION: No-code data engineering for domain expertsGathering Interest

Problem StatementCognite Data Fusion (CDF) offers a powerful suite of tools for industrial data operations, but its adoption remains limited to highly technical users such as data engineers, data scientists, and developers. Today, creating data transformations, writing functions, deploying models, and generating insights in CDF typically requires:Knowledge of Spark SQL for transformations Python programming for custom functions Understanding of data modeling concepts Manual deployment and orchestrationThis steep technical barrier restricts broader usage, particularly among domain experts like production operations engineers, maintenance supervisors, or process owners who possess deep contextual knowledge but lack coding skills. As a result, CDF usage and ROI are throttled by dependence on a small pool of technical resources. VisionEmpower every domain expert to become a CDF power user — without writing a single line of code. Proposed Solution: Cognite vision – AI-Powered No-Code ExperienceIntroduce Cognite VISION, an out-of-the-box AI agent integrated into CDF that uses LLMs to eliminate the need for coding expertise.With VISION, a user can simply ask:"Join sensor data from the compressor with maintenance logs and create a dashboard to predict downtime every 6 hours."VISION handles the rest:Interprets the intent using an LLM Writes Spark SQL transformations behind the scenes Creates and deploys Python functions for processing or inference Builds contextualized data models Schedules pipelines Deploys insights to dashboards or external appsAll within seconds, fully auditable, and explainable for enterprise transparency. Key Features Natural Language Interface: Ask for transformations, models, or dashboards in plain language Automatic Backend Generation: LLMs write code, configure parameters, and deploy pipelines One-Click Deployment: From request to production in a few clicks or a single prompt Insight Builder: Automatically recommends and generates insights based on domain context Governed Execution: Every AI-generated artifact passes through existing governance and logging frameworks