ARIA: Professional Risk Audit Infrastructure

A high-performance system for institutional risk intelligence. It cross-references forensic financial documentation with global market data to identify structural and clinical risks.

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Executive Summary

ARIA (Autonomous Risk Intelligence Agent) provides high-fidelity financial and structural auditing. The system automates the detection of anomalies within corporate and clinical reports by integrating Retrieval-Augmented Generation (RAG) and deterministic inference models.

👉 Optimized for low-latency responses, generating structured outputs within 30 seconds. 👉 Designed as a scalable full-stack system capable of handling real-time document analysis with optimized response latency.

Interface Preview

Deployment

• Frontend: Deployed on Vercel View Live Dashboard
• Backend Model: Hosted on Hugging Face Spaces

Architecture Diagram

Frontend (React / Next.js)
        ↓
FastAPI Backend
        ↓
ChromaDB (Vector DB)
        ↓
LLM (Groq)

View detailed dependency graph

graph LR
    A[Next.js Frontend] -->|REST API Requests| B[FastAPI Backend]
    B -->|Context Retrieval| C[(ChromaDB Vector Store)]
    B -->|Inference| D[Llama 3.3 / Groq LLM]
    C -->|Document Embeddings| B
    D -->|Risk Analysis| B
    B -->|JSON Response| A

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System Design

• Handles document ingestion pipeline
• Uses vector search for efficient retrieval
• Optimized API responses for low latency

System Architecture & Components

• Frontend: Next.js 14 dashboard providing real-time data streaming via Server-Sent Events (SSE).
• Backend API: Python FastAPI layer handling requests, orchestration, and integrations.
• Forensic RAG Infrastructure: ChromaDB vector storage combined with specialized sentence-transformer embeddings to query complex PDF reports.
• LLM Intelligence Mesh: High-performance inference via Groq/Llama 3.3 70B for institutional reasoning.
• Strategic Pulse Gateway: Real-time integration with NewsAPI for external risk fusion.

API Flow

1. Document Ingestion (/api/v1/upload): PDF files are uploaded, chunked, and embedded into ChromaDB.
2. Risk Query (/api/v1/analyze): The frontend sends an analysis request.
3. Retrieval: The backend performs semantic search against the vector database to retrieve highly relevant chunks.
4. Market Context: External APIs (e.g., NewsAPI) are pinged for live market conditions.
5. LLM Evaluation: Retrieved text and market data are processed by the LLM to compute a Risk Score (0-100).
6. Streaming Response: Results are streamed back to the frontend using Server-Sent Events.

Example Output Payload

{
  "status": "success",
  "data": {
    "risk_score": 82,
    "classification": "High Volatility",
    "findings": [
      "Inconsistent debt-to-equity ratio reported in Section 3.2.",
      "Recent market news indicates supply chain disruptions affecting raw material costs."
    ],
    "confidence_level": 0.94
  }
}

Environment & Deployment

Hardware & Engine Requirements

• Python 3.9+ Runtime
• Node.js 18+ (LTS)
• External API Gateways: Groq, NewsAPI

Infrastructure Initialization

A. Backend Intelligence Service (FastAPI)

cd backend
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
python main.py

B. Frontend Analytic Dashboard (Next.js)

cd frontend
npm install
npm run dev

Technical Roadmap

• Multi-Agent Orchestration: Implementation of graph-based reasoning loops to increase detection accuracy.
• SEC/EDGAR Integration: Direct ingestion of institutional filings via Ticker-based indexing.

License

Distributed under the MIT License. Professional use only.