title: AndesOps AI
emoji: 🧠
colorFrom: red
colorTo: gray
sdk: docker
pinned: false
app_port: 7860
AndesOps AI: Agentic Tender Intelligence
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.
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
cd backend
python -m venv .venv
.\.venv\Scripts\Activate.ps1
pip install -r requirements.txt
uvicorn app.main:app --reload --port 8000
Frontend Setup
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.