AIX-based architecture proposal integrating Equitus.ai KGNN & EVS with IBM's Hybrid Cloud, Data & AI, and Automation stack—tailored to support ROI justification through EVW/EEV metrics and Gen AI enablement.
📘 Custom AIX Architecture Proposal:
“Modernizing Mission-Critical Workloads with Gen AI and Equitus.ai”
1. 🎯 Executive Summary
This proposal outlines how integrating Equitus.ai's Knowledge Graph Neural Network (KGNN) and Video Sentinel (EVS) into IBM AIX environments can reduce ETL overhead, lower FTE demand, and increase mission-aligned outcomes. It provides stakeholders with a defensible business case to justify Gen AI investments through measurable EVW (Equivalent Value of Work) and EEV (Equivalent Employee Value) outcomes.
2. 🧩 Solution Stack Overview
| Layer | Components | Partners/Tech Stack |
|---|---|---|
| Edge & Input Layer | Sensors, Cameras, Network Logs | EVS (Equitus Video Sentinel) |
| AI Ingestion Layer | Data feeds, video streams, structured/unstructured | KGNN (Knowledge Graph Neural Network) |
| AIX Core Layer | IBM Power10, AIX OS, Virtual I/O Servers | IBM |
| Middleware | Red Hat OpenShift, IBM MQ, IBM Cloud Pak | IBM, Equitus, Watsonx |
| Data Fabric / ML | Watsonx, DataStage, Db2 AI, Instana, Turbonomic | IBM AI & Automation |
| User Interface Layer | Dashboards, Command & Control Apps, Reporting | Maximo, Equitus UX, Custom UIs |
3. 📉 Legacy Pain Points vs. Equitus-Enabled Improvements
| Pain Point | Improvement via Equitus.ai | EVW / EEV Impact |
|---|---|---|
| ETL process delays AI modeling | KGNN auto-generates structured knowledge from data | ↑ EVW, ↓ FTE for data prep |
| High analyst hours for video/audio review | EVS extracts and tags video metadata automatically | ↑ EEV per analyst, ↓ SOC costs |
| Data silos across workloads | KGNN federates and contextualizes disparate datasets | ↑ EVW across domains (Ops, Intel, Maint.) |
| Low explainability in AI outcomes | Transparent graph lineage and relevance scoring | ↑ Trust, faster adoption of Gen AI tools |
4. 📐 Reference Architecture Diagram
Let me generate a high-resolution architecture graphic showing:
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IBM AIX + Power10 as the backbone
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Equitus KGNN as the cognitive core for ingesting and linking data
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EVS processing video feeds at the edge
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Watsonx and IBM Cloud Paks using KGNN outputs for AI/ML
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Red Hat OpenShift enabling hybrid deployment
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Dashboards or operational UIs on top
🟢 Would you like the diagram in:
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PowerPoint Slide Format?
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High-resolution PNG or PDF?
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Whitepaper-style diagram with explainer notes?
5. 💰 Business Case Using EVW/EEV Metrics
| Metric | Legacy Cost Estimate | With Equitus.ai | Savings (%) | Value Expression |
|---|---|---|---|---|
| ETL Engineering Hours | $2.2M/year | $0.8M/year | 63% | ↑EVW = $1.4M annual |
| SOC Video Analysts | $1.5M/year | $0.6M/year | 60% | ↑EEV = $900K annual |
| Time-to-Model AI Workloads | 12–18 weeks | 3–4 weeks | 70% faster | Faster mission impact |
6. 📍 Deployment Model Options
| Deployment Type | Description | Use Case |
|---|---|---|
| On-Prem AIX | Full-stack on IBM Power10, closed-loop systems | Defense, CBP, Fed Integrators |
| Hybrid Cloud | Local KGNN + Watsonx on IBM Cloud/Red Hat OpenShift | Enterprise, Intelligence Fusion |
| Edge + Cloud | EVS on border edge; KGNN + Watsonx in cloud | Border surveillance, Public Safety |
7. 📞 Next Steps & Contact
🧪 Proof of Value:
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Available 60–90 day PoV with IBM + Equitus teams.
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Custom sandbox with redacted/simulated mission data.
📍 Contacts:
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IBM Federal AI Practice – ibmcloud@us.ibm.com
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Equitus.ai Federal Solutions – info@equitus.ai , +1 (813) 540-6740
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