ARIA

Adaptive Retrieval & Intelligent Analysis

AI-powered universal web extraction platform built for structured data intelligence.

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✨ Overview

ARIA is a modern AI-powered web extraction engine that transforms unstructured webpages into clean, structured, machine-readable data using advanced semantic analysis.

Instead of relying on fragile CSS selectors or hardcoded scraping logic, ARIA uses intelligent page understanding powered by Google Gemini AI combined with a stealth-capable extraction pipeline.

Designed for:

• E-commerce extraction
• Market research
• AI agents
• Knowledge aggregation
• Structured dataset generation
• Research automation

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🚀 Core Features

🌐 Universal Extraction

Extract structured data from virtually any website:

• E-commerce stores
• Documentation pages
• Blogs & articles
• Forums
• News websites
• Wikis
• Dynamic JavaScript applications

No manual selectors required.

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🧠 AI Semantic Structuring

ARIA uses a multi-pass AI analysis pipeline to:

• Understand page context
• Identify relevant entities
• Extract structured information
• Generate schema-clean JSON outputs

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🕶️ Stealth Rendering Engine

Built-in headless browsing system with:

• JavaScript rendering
• Anti-bot handling
• Dynamic page loading
• Fallback extraction pipelines

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📦 Structured Output

Export clean:

• JSON
• CSV
• Typed structured objects

Optimized for:

• AI workflows
• APIs
• Databases
• Analytics pipelines

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🔒 Privacy Focused

ARIA processes extraction through your own infrastructure and API keys.

Your data remains under your control.

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🏗️ System Architecture

ARIA follows a modular dual-service architecture:

Frontend (Next.js)
        ↓
FastAPI Backend
        ↓
Extraction Engine
        ↓
Gemini AI Structuring
        ↓
Structured JSON Output

Frontend

Modern glassmorphism interface built with:

• Next.js 14
• React
• TailwindCSS
• Framer Motion

Backend

High-performance FastAPI service responsible for:

• scraping orchestration
• rate limiting
• AI classification
• semantic extraction
• fallback handling

Detailed architecture diagrams and flows are available in:

docs/architecture.md

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⚡ Quick Start

1. Clone Repository

git clone https://github.com/Chaitya44/aria-webscraper.git
cd aria-webscraper

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2. Backend Setup

cd backend

python -m venv venv

# Windows
.\venv\Scripts\Activate.ps1

# Mac/Linux
source venv/bin/activate

pip install -r requirements.txt

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

Backend runs on:

http://localhost:8000

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3. Frontend Setup

cd frontend/nexus-scraper-ui

npm install

npm run dev

Frontend runs on:

http://localhost:3000

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🔑 Environment Variables

Backend

Create a .env file inside backend/

FIRECRAWL_API_KEY=
GEMINI_API_KEY=

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Frontend

Create a .env.local file inside frontend/nexus-scraper-ui/

NEXT_PUBLIC_FIREBASE_API_KEY=
NEXT_PUBLIC_FIREBASE_AUTH_DOMAIN=
NEXT_PUBLIC_FIREBASE_PROJECT_ID=
NEXT_PUBLIC_FIREBASE_STORAGE_BUCKET=
NEXT_PUBLIC_FIREBASE_MESSAGING_SENDER_ID=
NEXT_PUBLIC_FIREBASE_APP_ID=

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🌍 Deployment

Frontend

Recommended platforms:

• Vercel
• Netlify

Deploy directory:

frontend/nexus-scraper-ui

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Backend

Recommended platforms:

• Render
• Railway
• Ubuntu VPS
• Docker

Start production server:

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

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🛠️ Tech Stack

Frontend

• Next.js 14
• React
• TypeScript
• TailwindCSS
• Framer Motion

Backend

• FastAPI
• Python
• Gemini AI
• Firecrawl
• AsyncIO

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🧭 Roadmap

• Scheduled scraping
• Workflow automation
• Browser extension
• Team workspaces
• API dashboard
• Scraping templates
• Real-time extraction monitoring

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📁 Project Structure

aria-webscraper/
│
├── backend/
├── frontend/
├── docs/
│
├── README.md
├── LICENSE
└── .gitignore

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📄 License

MIT License

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Built with AI-driven extraction intelligence.