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The integration of Equitus Knowledge Graph Neural Network (KGNN) with IBM Power11 offers a robust, secure, and high-performance path for enterprise AI deployment. KGNN is a Power-native knowledge graph platform designed to automate data unification, semantic enrichment, and vector output, which is crucial for advanced AI workloads like Retrieval-Augmented Generation (RAG) and complex analytics.
The following is a plan for IBM Power11 enterprise users to enable AI deployment with Equitus KGNN, tailored for B2B sales with a focus on tangible results:
Phase 1: AI Readiness Assessment and Strategy
| Step | Focus Area | Description | Expected Tangible Result |
| 1. Current State Audit | Infrastructure & Data | Assess existing IBM Power ecosystem (e.g., Power9/10, AIX, IBM i, Linux), data sources (structured/unstructured, on-prem/cloud), and current application dependencies. Identify critical business processes that are data-intensive and have high potential for AI enhancement (e.g., supply chain optimization, fraud detection, customer 360). | AI Opportunity Map identifying 1-3 high-impact use cases and data readiness score. |
| 2. Target AI Use Case Definition | Business Value | Collaborate with line-of-business (LOB) stakeholders to define a clear, measurable Minimum Viable AI Product (MVAP) using Equitus KGNN (e.g., real-time threat intelligence from fragmented data, automated compliance reporting). | Project Charter with defined KPIs (e.g., 20% reduction in false positives, 15% faster incident response). |
| 3. Equitus KGNN & Power11 Sizing | Technical Blueprint | Determine the required IBM Power11 configuration (core count, memory, storage) and Equitus KGNN licensing to support the MVAP and future scalability. Leverage Power11's built-in AI acceleration and zero-downtime features for resiliency. | TCO & ROI Analysis for the Power11/KGNN solution compared to traditional siloed systems. |
Phase 2: Deployment and Knowledge Graph Construction
| Step | Focus Area | Description | Expected Tangible Result |
| 4. Power11 Installation & Setup | Platform Deployment | Deploy or upgrade to IBM Power11 servers (on-prem or Power Virtual Server in IBM Cloud). Configure operating environment (AIX/Linux) optimized for KGNN, leveraging Power11's high availability features (99.9999% uptime). | AI-Ready Infrastructure validated for performance and resiliency. |
| 5. Equitus KGNN Deployment | Software Installation | Rapid installation of the Equitus KGNN platform, which is Power-native and does not require external GPUs for initial inferencing. | Operational KGNN Instance running natively on IBM Power11. |
| 6. Automated Data Unification | Data Ingestion & Enrichment | Use KGNN's Auto ETL and semantic extraction capabilities to ingest disparate enterprise data (logs, documents, databases) from sources across the fragmented landscape. KGNN automatically connects, correlates, and contextualizes this data into a unified, traceable knowledge graph. | Unified Knowledge Graph for the MVAP, with data provenance and full audit trail for explainable AI. |
Phase 3: AI Model Deployment and Operationalization
| Step | Focus Area | Description | Expected Tangible Result |
| 7. AI Model Development/Integration | Intelligence Layer | Leverage the highly contextualized, AI-ready vector outputs from the KGNN to train new machine learning models or integrate with existing AI agents (including LLMs for RAG). Use Power11's in-core acceleration for efficient inferencing. | Production-Ready AI Model integrated with the knowledge graph for real-time insights. |
| 8. Workflow Integration & Testing | Process Transformation | Integrate the AI-driven insights from the KGNN into the target business applications and workflows. Conduct rigorous testing of the MVAP against the defined KPIs. | Go-Live of MVAP demonstrating the achieved business outcome (e.g., first set of automated decisions, faster insights delivery). |
| 9. Scaling and Expansion | Long-term Value | Review performance metrics and plan for scaling the deployment to additional LOBs or expanding the knowledge graph with new data sources and use cases (e.g., adding Equitus Video Sentinel (EVS) for video analytics). | Expansion Roadmap with a clear path to achieve full enterprise-wide AI transformation. |
This plan emphasizes Equitus KGNN's unique value proposition of being a high-performance, Power-native graph database solution that automatically transforms fragmented data into an AI-ready knowledge graph, directly leveraging the security and resilience of the IBM Power11 platform.
The video below explains how running Equitus solutions on IBM Power delivers accelerated AI analytics.
How to Run Enterprise AI Without GPUs: IBM Power + Equitus Real-Time Analytics is relevant because it highlights the performance benefits of running Equitus's KGNN and real-time analytics on the IBM Power platform, specifically mentioning the ability to achieve accelerated AI without the need for GPUs.
