Knowledge Generation (KoGen) is the process by which multiple information is systematically gathered, compiled and integrated. Kogen systems provide users with the ability to connect with Real Time Data from prompt to Platform. Enterprises can enable MoA, multiple agents running simultaneously to create intelligence with Chain of Trust to help make decisions and perform tasks.
Equitus is offering this technology in partnership with IBM. Equitus is in the process of connecting a framework for IBM Power 10, Maximo and
Enterprise Information Pipeline: Equitus 7 produces a platform to enable [Knowledge Generation]
Designed to Enable fast, scalable and safe Enterprise AI on IBM
Downstream (Data Collection) - Information at the edge, legacy systems
[Knowledge Graph]
- Edge Devices (Wells): Equitus Sentinel - Collect raw data from various sources (sensors, IoT devices, etc.)
- Data Ingestion (Gathering): Aggregate data from edge devices into a central repository - Equitus Systems Integration -
Midstream (Data Processing) - Refining the sourced data into inferential and semantic information
[Knowledge Engine]
- KGNn (Refinery): Process and refine data using knowledge graphs, entity recognition, and AI-driven insights
- Data Enrichment (Blending): Combine processed data with external sources (knowledge graphs, ontologies, etc.)
- Data Transformation (Cracking): Convert data into actionable formats for upstream consumption
Upstream (Decision Support)
[Knowledge Assistant]
- Agent (Petrochemical Plant): Receive refined data and generate recommendations, predictions, or actions
- Decision Support Systems (Pipelines): Integrate agent outputs into business applications, workflows, or interfaces
- Actionable Insights (Products): Deliver AI-driven insights to end-users, stakeholders, or systems
Pipeline Infrastructure
- APIs (Pipelines): Connect upstream, midstream, and downstream components
- Data Storage (Tank Farms): Store and manage data across the pipeline
- Security and Governance (Pipeline Protection): Ensure data integrity, access control, and compliance
Refining and Optimization
- Continuous Learning (Drilling): Refine AI models and knowledge graphs through feedback loops
- Performance Monitoring (Flow Measurement): Track pipeline efficiency, data quality, and agent performance
- Optimization (Enhanced Recovery): Apply AI-driven optimization techniques to improve pipeline efficiency
This analogy maps the AI information pipeline to the oil industry's upstream, midstream, and downstream processes, highlighting the connections between data collection, processing, and decision support.
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