IBM EVS - Interconnect IBM Power (Power10/Power11) infrastructure to transform raw, multimodal data into secure, actionable intelligence.

IBM EVS proposes a defined operational capability at a fixed scope and fee—not an open-ended AI integration "project/package." 

IBM EVS as a productized cyber-physical integration service: connect cameras and enterprise systems, turn observations into contextualized events, run suitable AI locally, and use GPU cloud capacity selectively.

Equitus Intelligent Ingestion Systems—specifically combining Equitus Video Sentinel (EVS) and the KGNN (Knowledge Graph Neural Network) platform—interconnect IBM Power (Power10/Power11) infrastructure to transform raw, multimodal data into secure, actionable intelligence.

1. Power10/Power11 Integration & On-Prem MMA to Cloud GPU Bridging

• Matrix Math Assist (MMA): Equitus leverages IBM Power10/Power11 built-in MMA capabilities to run deep learning, computer vision, and graph neural network processing directly on-premises. This allows high-performance AI inference (like video frame analytics and graph extraction) at the edge or core without requiring local GPUs.

• Hybrid On-Prem-to-Cloud Bridge: While routine localized workloads run securely on-prem via Power servers using MMA, the architecture supports a secure bridge to cloud-based GPU clusters when massive model training, large-scale LLM fine-tuning, or federated tasks demand it.

• Red Hat Enterprise Linux (RHEL): Optimized natively for RHEL (and containerized via OpenShift environments), the stack provides a consistent, enterprise-grade operating foundation that spans from your local Power data center nodes out to cloud environments.

2. Video Intelligence Meets Knowledge Graphs (EVS + KGNN)

• Automated Video Ingestion (EVS): EVS ingests analog and digital video streams, auto-indexing every frame to track objects, behaviors, and attributes (e.g., vehicle types, clothing patterns, or loitering).

• Semantic Unification via KGNN: Instead of keeping video metadata locked in a siloed Video Management System (VMS), Equitus KGNN ingests these structured streams alongside unstructured documents, logs, and relational databases. It dynamically builds a schema-less, self-constructing knowledge graph, mapping the real-world context and relationships between physical security events and core IT data.

3. Standards-Based Querying and AI (SPARQL & Vectorization)

• SPARQL Semantic Queries: Using semantic web standards like SPARQL, users can execute precise, federated graph queries against complex interconnected nodes and edges—querying explicit relationships across disparate data silos instantly.

• AI & RAG Readiness: KGNN converts graph structures into vectorized outputs. This seamlessly feeds Retrieval-Augmented Generation (RAG) pipelines, Large Language Models (LLMs), and machine learning applications with verified, context-rich enterprise data.

4. Human-in-the-Loop (HITL) & Professional Services

• Operational Control: Automated anomaly detection (via EVS) and graph insights flag potential threats or correlations, but critical decision nodes incorporate Human-in-the-Loop validation. Operators review high-stakes alerts, provide feedback, and confirm system classifications to continuously tune the intelligence models.

• Consulting, Deployment, and Testing Services: Implementing a unified Power-native architecture involves expert deployment strategies—spanning secure RHEL tuning, custom data-source connectors, SPARQL schema design, and end-to-end performance validation—ensuring the system complies with rigorous governance standards (such as CJIS, NERC CIP, or HIPAA) while minimizing infrastructure overhead.