README.md

---
title: AndesOps AI
emoji: 🧠
colorFrom: red
colorTo: gray
sdk: docker
pinned: false
app_port: 7860
---

# AndesOps AI: Agentic Tender Intelligence


[![AMD Powered](https://img.shields.io/badge/AMD-Powered-ED1C24?style=for-the-badge&logo=amd&logoColor=white)](https://www.amd.com/en/developer/resources/ai-developer.html)
[![ROCm](https://img.shields.io/badge/ROCm-Optimized-blue?style=for-the-badge)](https://rocm.docs.amd.com/)
[![Next.js](https://img.shields.io/badge/Next.js-14-black?style=for-the-badge&logo=next.js)](https://nextjs.org/)
[![FastAPI](https://img.shields.io/badge/FastAPI-Framework-009688?style=for-the-badge&logo=fastapi)](https://fastapi.tiangolo.com/)

**AndesOps AI** is a state-of-the-art business intelligence platform designed to transform the complex landscape of public procurement in Chile (Mercado Público) into actionable strategic advantages. Built for the **AMD Developer Hackathon**, it leverages a sophisticated **Agentic Multi-Agent System** to analyze technical and administrative bases with unprecedented speed and precision.

---

## 🚀 The Challenge
Public bidding processes are notoriously document-heavy, requiring legal, technical, and strategic expertise to evaluate a single opportunity. Companies often miss deadlines or overlook critical risks buried in 100+ page PDFs.

## 🧠 The Agentic Solution: "The Virtual Board of Experts"
AndesOps AI moves beyond simple chatbots. It deploys a **coordinated panel of AI agents** that work in parallel to evaluate every tender:

- ⚖️ **Legal & Compliance Agent**: Scans for administrative hurdles, critical deadlines, and compliance gaps.
- 🏗️ **Technical Architect Agent**: Maps tender requirements to the company’s specific tech stack and experience.
- 📊 **Strategy & ROI Agent**: Analyzes competition, calculates potential ROI, and defines a "Winning Strategy".
- 🧠 **The Orchestrator**: Consolidates agent reports into a final **Strategic Fit Score** and an executive summary.

---

## 🛠️ Architecture & AMD Integration
AndesOps AI is engineered to scale using high-performance compute:

- **Hardware Acceleration**: Optimized to run on **AMD Instinct™ MI300X GPUs** via the **AMD Developer Cloud**.
- **Software Stack**: Built on **ROCm™** for high-throughput inference, allowing simultaneous processing of multiple massive tender documents without bottlenecks.
- **Backend**: **FastAPI** with asynchronous task execution for parallel agent processing.
- **Frontend**: **Next.js 14** with a premium, enterprise-ready UI/UX.

### **Modern High-Performance Architecture**
AndesOps AI is built for massive document analysis using a tiered approach that prioritizes hardware-accelerated inference.

```mermaid
graph TD
    %% Node Styles
    classDef client fill:#0ea5e9,stroke:#fff,stroke-width:1px,color:#fff;
    classDef logic fill:#8b5cf6,stroke:#fff,stroke-width:1px,color:#fff;
    classDef hardware fill:#ec4899,stroke:#fff,stroke-width:2px,color:#fff;
    classDef data fill:#64748b,stroke:#fff,stroke-width:1px,color:#fff;

    %% Client Tier
    subgraph Client_Tier [Enterprise UI Layer]
        UI["<b>AndesOps AI Dashboard</b><br/>Next.js 14 + Tailwind CSS"]
        UI --- |Real-time Stream| WS[WebSocket / API]
    end

    %% Orchestration Tier
    subgraph Orchestration_Tier [Multi-Agent Consensus War Room]
        WS --> AgentManager[<b>Consensus Orchestrator</b>]
        AgentManager --> Agent1[⚖️ Dra. Legal]
        AgentManager --> Agent2[🛠️ Ing. Técnico]
        AgentManager --> Agent3[📈 Sra. Estrategia]
    end

    %% Compute Tier
    subgraph Compute_Tier [<b>AMD HIGH-PERFORMANCE COMPUTE</b>]
        Agent1 & Agent2 & Agent3 --> |Direct ROCm Link| ROCm[<b>ROCm™ 6.1 Stack</b>]
        ROCm --> vLLM[vLLM Inference Server]
        vLLM --> MI300X["<b>AMD Instinct™ MI300X</b><br/>(Private Compute Node)"]
    end

    %% Data Tier
    subgraph Data_Tier [Intelligence & Data]
        AgentManager -.-> MP[Mercado Público API]
        AgentManager -.-> Scraper[Intelligent Scraper]
        MP & Scraper --> DB[(SQL Persistence)]
    end

    %% Apply Styles
    class UI,WS client;
    class AgentManager,Agent1,Agent2,Agent3 logic;
    class ROCm,vLLM,MI300X hardware;
    class MP,Scraper,DB data;
```

---

## 💻 Setup & Installation

### **Prerequisites**
- Python 3.10+
- Node.js 18+
- AMD ROCm (Optional for local acceleration)

### **Backend Setup**
```powershell
cd backend
python -m venv .venv
.\.venv\Scripts\Activate.ps1
pip install -r requirements.txt
uvicorn app.main:app --reload --port 8000
```

### **Frontend Setup**
```powershell
cd frontend
npm install
npm run dev
```

### **Environment Variables**
Copy `.env.example` to `.env` and configure:
- `GEMINI_API_KEY`: For LLM orchestration (or your AMD local endpoint).
- `MERCADO_PUBLICO_TICKET`: For real-time tender syncing.

---

## 📈 Business Value
- **Efficiency**: Reduce manual analysis time by over 90%.
- **Risk Mitigation**: Early detection of legal traps and technical gaps.
- **Competitiveness**: Generate high-quality proposal drafts aligned with specific tender scoring criteria.

## 📄 License
MIT License - Developed for the **AMD Developer Hackathon 2026** with ❤️ by the AndesOps Team, powered by [REW](https://www.rew.cl).