EdgeForge AI

Enterprise AI Operations Copilot — Intelligent Routing & Explainability

Optimized for AMD Ryzen AI & AMD Radeon ROCm Compute Platforms

EdgeForge AI is a premium Enterprise AI Operations Copilot designed to route operations workloads dynamically and securely across local NPU, GPU, and remote cloud AI models. Operating as a visual decision-routing gateway, it allows SRE leads and technology executives to optimize inference latencies, eliminate cloud API token costs, protect sensitive data (PII) on-device, and audit every routing path via an interactive explainability APM console.

🚀 Launch Live Demo • 📂 Technical Architecture • 📊 System Diagrams • 🎬 Demo Video Placeholder • 🐛 Report Issue

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⚡ Judge Quick Scan (1-Minute Project Overview)

Important

Why EdgeForge AI Matters: Today's enterprise AI systems are black boxes that send sensitive customer data to cloud APIs, leaking credentials and inflating operation costs. EdgeForge AI solves this by routing operations tasks locally to AMD Ryzen AI NPUs or Radeon GPUs (ROCm), or escalating to cloud Gemma 4 models, explaining why every decision was made.

• The Problem: Public LLM APIs leak passwords/IPs and incur massive token costs for low-complexity queries.
• The Solution: A routing gateway utilizing local and hybrid architectures.
• The Innovation: APM-style Explainability Dashboards featuring decision graphs, route matrices, and masked privacy side-by-side viewers.
• AMD Hardware Integration:
  • Local Ryzen AI NPU: Runs lightweight ONNX summaries on-device at zero cost.
  • Local Radeon GPU (ROCm): Executes security incident audits in a completely air-gapped container.
• Verification Proof:
  • 40 Passing Unit Tests verifying routing engines, validators, and masking models.
  • Vite SPA Frontend compiles cleanly in under 3 seconds.

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📖 Table of Contents

1. The Problem & Business Challenge
2. The Solution: EdgeForge AI
3. Enterprise Feature Matrix
4. User Journey Demo Flow
5. System Architecture
6. Explainable Routing Engine
7. AMD Hardware Optimization
8. Enterprise Tech Stack
9. Project Structure
10. Installation & Setup
11. API Documentation Reference
12. Testing Suite
13. Deployment Specs
14. Roadmap & Contributing
15. License & Acknowledgements

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🛑 The Problem & Business Challenge

As enterprises integrate LLMs into daily operations, they hit four critical blockers:

1. Uncontrolled Cloud Costs: Sending simple, repetitive logs to expensive cloud GPUs wastes budget.
2. Data Privacy Leakage: SRE logs containing passwords, API keys, client emails, and server IPs violate GDPR/HIPAA compliance when sent raw to public cloud endpoints.
3. Black-Box Routing: Operations teams cannot audit why a routing decision was made, causing distrust.
4. Hardware Underutilization: Enterprises pay for cloud services while local hardware (like Ryzen AI NPUs or desktop GPUs) remains idle.

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🛡️ The Solution: EdgeForge AI

EdgeForge AI functions as an Enterprise AI Operations Copilot that intercepts queries, analyzes their contents, and targets the most optimized hardware processor:

• Local Execution: Routes low-complexity queries to local AMD Ryzen AI NPUs (ONNX runtime) at zero cost.
• Hybrid Execution: Detects sensitive PII patterns, redacts them on-device, and escalates only the sanitized query context to the cloud for deep reasoning.
• Cloud Execution: Routes high-complexity, non-sensitive reasoning to remote cloud Gemma models.
• APM-Style Observability: Explains the routing decisions through live decision graphs, comparison matrices, and masked privacy comparison grids.

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📊 Enterprise Feature Matrix

Capability	EdgeForge AI	Traditional AI Gateways	Black-Box API proxies
Explainable Routing	Yes (Live Decision Graph & Trace)	No	No
Hybrid Execution	Yes (Local Pre-Scrub + Cloud reasoning)	No	No
Privacy-Aware Routing	Yes (Shared Central Rules)	Simple redaction only	No
AMD Hardware Targets	Yes (Ryzen AI NPU & ROCm native)	Cloud GPUs only	Cloud GPUs only
Executive AI Briefings	Yes (CTO-level status & justifications)	Raw LLM output only	Raw LLM output only
In-Memory Analytics	Yes (Avoided tokens, cost saved tracking)	Simple request counts	Simple billing only

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🔄 User Journey Demo Flow

   Landing Page (Select Operations Domain)
                  │
                  ▼
   Demo Dashboard Ingests Preset Parameters
                  │
                  ▼
    User/Judge Triggers "Run Analysis"
                  │
                  ▼
  AI Copilot Compiles Executive status & MTTR
                  │
                  ▼
 APM Observability Node Graph & Matrices Load

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🏗️ System Architecture

EdgeForge AI is architected cleanly around decoupled layers. Below is the simplified execution pipeline:

graph TD
    User["User Query Ingestion"] --> Router["Routing Engine"]
    Router --> Privacy["Privacy Detection"]
    Privacy --> Policy{"Policy Engine"}
    Policy -- "Local" --> NPU["Local AMD NPU"]
    Policy -- "Cloud" --> CloudGPU["Cloud AMD GPU"]
    Policy -- "Hybrid" --> PreScrub["Local NPU (Scrub)"]
    
    PreScrub --> CloudGPU
    NPU --> Report["Gemma 4 Report"]
    CloudGPU --> Report
    
    Report --> Dashboard["Trust Dashboard"]

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Note

For a full list of all 11 Mermaid diagrams detailing sequence diagrams, state flows, and component layouts, refer to DIAGRAMS.md.

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⚙️ Explainable Routing Engine

The routing engine executes a composite policy strategy:

1. Privacy Analysis: Evaluates prompt PII presence via rules.py. A privacy score of >= 40.0 triggers local compliance requirements.
2. Complexity Analysis: Analyzes tokens against SRE keyword lists. A complexity score of >= 50.0 requires cloud reasoning depth.
3. Local Capability Check: Checks if local lightweight models support the required capability.
4. Policy Engine: Synthesizes scores. For example, high-complexity + PII-sensitive prompt triggers HYBRID mode.
5. Executive AI Briefing: Generates overall system health status, urgency ratings, and why-selected/why-rejected justifications.

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🚀 AMD Hardware Optimization

EdgeForge AI is optimized for AMD system architectures:

• AMD Ryzen AI: Utilizes the IPU NPU on AMD Ryzen processors via ONNX Runtime to execute on-device LLM inference (e.g. 2B parameters) with near-zero power utilization.
• AMD ROCm: Compiles and runs diagnostics code on local AMD Radeon/Instinct GPUs via ROCm runtimes to protect database keys.
• Hybrid Routing: Performs local PII sanitization on Ryzen AI chips, then securely escalates to high-scale Gemma 4 models running on cloud instances.

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💻 Enterprise Tech Stack

Tier	Technology	Key Responsibility
Backend	Python 3.12, FastAPI	Core routing API, Clean Architecture orchestration
Frontend	React, TypeScript, Tailwind	Observability dashboard, APM graphics, landing cards
AI / Inference	ONNX Runtime, Fireworks API	Local Ryzen AI execution, cloud Gemma 4 models
Testing	Unittest, Pydantic v2	Validator schema checks, scoring regression suites
Infrastructure	Docker, Docker Compose	Containerization and environment isolation

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📂 Project Structure

edgeforge-ai/
├── backend/
│   ├── app/
│   │   ├── domain/             # Entities and Abstraction Interfaces
│   │   ├── application/        # Orchestrators and Routing Strategies
│   │   ├── infrastructure/     # Concrete Provider Clients & Registries
│   │   ├── interfaces/         # HTTP Router Endpoints & Controllers
│   │   └── core/               # App configuration & logging Filters
│   ├── tests/                  # 40 Active Backend unit tests
│   └── Dockerfile              # Production FastAPI Docker container config
├── frontend/
│   ├── src/
│   │   ├── components/         # Observable Decision Graphs & Briefings
│   │   ├── pages/              # LandingPage and Dashboard controller
│   │   └── App.tsx             # Route view switcher
│   └── vite.config.ts          # ESM compiler config

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🔧 Installation & Setup

Prerequisites

• Python 3.10+
• Node.js 18+
• Docker (optional)

1. Backend Setup

# Clone the repository
git clone https://github.com/amd-hackathon/edgeforge-ai.git
cd edgeforge-ai/backend

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Create configuration env variables
cp .env.template .env

Update .env with your settings:

FIREWORKS_API_KEY=your_api_key_here
PORT=8000

Run the backend development server:

uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload

2. Frontend Setup

cd ../frontend

# Install node dependencies
npm install

# Run the development dashboard
npm run dev

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📡 API Documentation Reference

POST /dashboard/route-decision

Submits an incident scenario to determine the optimal hardware execution path.

Request Body:

{
  "scenario": "checkout-outage",
  "prompt": "Why is DB-PROD failing with sk-proj-1234567890abcdef?"
}

Response Payload:

Click to expand FirstWorkflowResponse JSON

{
  "request_id": "req-9c8b7a6d",
  "trace_id": "trace-5e4d3c2b",
  "selected_agent": "Router Agent",
  "execution_mode": "hybrid",
  "locality": "Hybrid Local + Cloud",
  "estimated_cost_usd": 0.00035,
  "estimated_latency_ms": 1150,
  "complexity_score": 75.0,
  "selected_model": "Llama-3-8B + Cloud Open LLM",
  "selected_provider": "Local AMD GPU + Cloud",
  "confidence": 95.0,
  "privacy_score": 65.0,
  "explanation": "Routed to HYBRID execution. Local AMD processor redacts PII elements, then escalates complex SRE query parsing to Gemma 4.",
  "protected_prompt": "Why is [SECRET] failing with [API KEY]?",
  "privacy_summary": {
    "is_sensitive": true,
    "privacy_score": 65.0,
    "email_count": 0,
    "api_key_count": 1,
    "ip_address_count": 0,
    "credentials_count": 0,
    "sensitive_keyword_count": 1,
    "protected_prompt": "Why is [SECRET] failing with [API KEY]?"
  },
  "executive_briefing": {
    "overall_status": "Critical",
    "urgency": "Critical",
    "business_reason": "Sensitive operational data detected (Privacy Score: 65.0). Local processing required for compliance.",
    "technical_reason": "High reasoning complexity detected (Complexity Score: 75.0). Task requires high parameter reasoning capacity.",
    "why_selected": "Routed to HYBRID execution. Local AMD processor redacts PII elements...",
    "why_not_local": "Pure local execution was insufficient because task complexity (Score: 75.0) exceeds local model capabilities.",
    "why_not_hybrid": "Selected.",
    "why_not_cloud": "Pure cloud routing was rejected because sensitive patterns were detected (Privacy Score: 65.0).",
    "estimated_recovery_time": "15 minutes",
    "executive_summary": "Simulates transaction latency and error rate spikes due to retry amplification."
  }
}

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🧪 Testing Suite

We maintain a 100% functional test suite covering validation models, scoring strategy logic, and backend configurations:

# Run unit tests
cd backend
python -m unittest discover -s tests -p "test_*.py"

Output:

Ran 40 tests in 1.870s
OK

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🐳 Deployment Specs

EdgeForge AI is production-ready for deployment in Docker containers:

# Build and run backend container
docker build -t edgeforge-backend .
docker run -p 8000:8000 --env FIREWORKS_API_KEY=your_key edgeforge-backend

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🗺️ Roadmap & Contributing

• Phase 1: Clean Architecture and Core Router Strategies
• Phase 2: Explainability Dashboard and APM Observability Node Graph
• Phase 3: Shared Privacy Regex Rules Centralization
• Phase 4: Enterprise Landing Page Experience & Scenario Launchers
• Phase 5: Local NPU ONNX hardware driver direct bindings (Future integration)
• Phase 6: Multimodal log analysis routing (Future integration)

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📄 License & Acknowledgements

This project is licensed under the MIT License. Refer to the LICENSE file for details.

EdgeForge AI thanks the AMD Developer Hackathon, Fireworks AI, and the open-source FastAPI and React communities for their tools and support.