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Edge AI Networking Architecture

AI-Assisted Documentation

Abhavtech Phase 4 - Edge AI Deployment

Welcome to the comprehensive technical documentation for Abhavtech's Edge AI networking architecture. This documentation covers the deployment of distributed AI inference at Mumbai and Chennai hub sites, featuring industry-first Edge AI + Observability Fusion architecture.

What is Edge AI + Observability Fusion?

Unlike traditional edge AI deployments that operate in isolation, Abhavtech's architecture integrates edge AI inference with centralized observability platforms (Splunk MLTK, ThousandEyes, AppDynamics) to enable:

  • High-confidence automated decisions (<5% false positive rate vs. 15-30% traditional edge AI)
  • Multi-source validation (AI + ISE + BMS + historical patterns)
  • Real-time response (<500ms detection-to-action vs. 10-30 minutes manual review)
  • Privacy-first design (no video egress to cloud, no facial recognition)

Quick Navigation

Chapter 1: Executive Summary & Edge AI Vision

Strategic overview, business context, and deployment scope for Mumbai and Chennai hub sites.

Chapter 2: Use Case Architecture

Detailed architecture for three core use cases: physical security, building automation, and safety compliance.

Chapter 3: Platform Architecture

Complete platform design including multi-layer architecture, AI model pipeline, and multi-site synchronization.

Chapter 4: Integration Architecture

Integration specifications for 9 observability and security platforms.

Appendices

Project summary, hardware specifications, and reference materials.

Key Differentiators

✅ Edge AI + Observability Fusion - Multi-source validation for high-confidence decisions
✅ AgenticOps WF-009 - Automated actions with comprehensive guardrails
✅ Zero Trust Foundation - SGT-95 isolation, MACsec encryption, ISE integration
✅ Privacy-First - No facial recognition, no video egress to cloud
✅ Production-Ready - Built on Phases 1-3 infrastructure (Zero Trust, Observability, AI-Ready Network)

Deployment Overview

Site Edge AI Nodes Cameras Use Cases Timeline
Mumbai 2 (Primary + Standby) 135 UC1 + UC2 + UC3 Weeks 1-8
Chennai 2 (Primary + Standby) 135 UC1 + UC2 + UC3 Weeks 9-12
Total 4 nodes 270 cameras 3 use cases 16 weeks

Platform: Cisco UCS XE9305 + XE130c M8 + NVIDIA L4 24GB GPU
Network: Catalyst 9300/9500 with SD-Access fabric
Integration: ISE, Splunk, ThousandEyes, AppDynamics, BMS, FTD, XDR, ServiceNow

Architecture at a Glance

Abhavtech's existing hub sites run cameras and building systems on a Catalyst network, with security handled by manual video review and HVAC on fixed schedules. Phase 4 adds one new layer — Edge AI compute — that reuses all of the existing network and building infrastructure. Click either diagram to open it full-size.

Before — Existing Infrastructure:

flowchart LR
    CAM["Cameras &<br/>Building Sensors"] --> NET["Catalyst 9300/9500<br/>Network (existing)"]
    NET --> VMS["Video Recording<br/>+ Manual Review"]
    NET --> BMS["Honeywell BMS<br/>Scheduled HVAC"]
    VMS --> TEAM["Security Team<br/>10-30 min response"]

    classDef base fill:#eef2f7,stroke:#7a8ba0,color:#1a2b3c;
    class CAM,NET,VMS,BMS,TEAM base;

After — With Edge AI:

flowchart LR
    CAM["Cameras &<br/>Building Sensors"] --> NET["Catalyst 9300/9500<br/>Network (reused)"]
    NET --> AI["Edge AI Layer<br/>UCS XE130c M8 + L4 GPU<br/>(new)"]
    AI --> AUTO["Automated Response<br/>&lt;500ms detect-to-action"]
    AI --> BMS["Honeywell BMS<br/>Occupancy-driven HVAC"]
    AI --> OBS["Observability Fusion<br/>Splunk / ThousandEyes / AppD"]

    classDef base fill:#eef2f7,stroke:#7a8ba0,color:#1a2b3c;
    classDef new fill:#d7ebf8,stroke:#1B6CA0,color:#0d2b40,font-weight:bold;
    class CAM,NET,BMS base;
    class AI,AUTO,OBS new;

Documentation Scope & Roadmap

This documentation covers the Design & Architecture phase of the Edge AI deployment (Chapters 1–4), which is complete and validated:

  • Chapter 1 — Executive summary, business context, and deployment scope
  • Chapter 2 — Use case architecture (physical security, building automation, safety & compliance)
  • Chapter 3 — Edge AI platform architecture (multi-layer design, AI model pipeline, multi-site model)
  • Chapter 4 — Integration architecture across nine observability and security platforms

The following content is planned for subsequent phases and is not yet included:

  • Implementation Roadmap — Week-by-week deployment plan (Mumbai pilot, Chennai rollout, production hardening)
  • Operations & Monitoring — Runbooks, alerting tiers, and troubleshooting procedures
  • Security Deep-Dive — Threat model, SGACL micro-segmentation, and compliance mapping (GDPR, SOC 2)
  • Reference Appendices — ISE/BMS configuration, AgenticOps WF-009 YAML, Splunk dashboards, and model-training detail

This scoping reflects a deliberate sequencing: architecture and design are finalized first, with detailed implementation and operations content to follow.

About This Documentation

This technical documentation was developed with Claude AI assistance to demonstrate enterprise-grade documentation for complex network and AI infrastructure projects. All content is transparently marked as AI-assisted and based on production-grade architecture patterns.

Documentation Framework: MkDocs Material
Version: 1.0 (April 2026)
Author: Raj Mohan, Principal Consultant - Unified Communications & Contact Center Solutions

For questions or feedback: abhavtech.com