🤖 AI Business Analyzer

An autonomous AI-powered business analysis system that researches markets, stores historical insights, and generates structured strategic reports — using persistent memory, live web data, and a clean Streamlit interface.

🔗 Live Demo

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🚀 Features

• Modular agent architecture for clean separation of concerns
• Goal-to-task planning powered by Gemini LLM
• Live web search via Serper.dev API
• Persistent memory with SQLite for historical insight storage
• Structured report generation with actionable business intelligence
• SWOT analysis tab for dedicated strategic breakdown
• Chat interface for interactive follow-up queries
• PDF export for saving and sharing reports
• Streamlit front-end for an intuitive user experience
• UV-based dependency management for fast, reliable installs

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🧠 Architecture Overview

User Goal
  → Planner Module
  → Web Search Tool (Serper.dev)
  → Result Aggregator
  → Context Memory
  → Report Generator
  → Memory Storage (SQLite)
  → Streamlit UI Output (Chat / History / SWOT tabs)

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🗂 Project Structure

├── Visuals
│   └── AI-Business-Analyzer.png
├── agents
│   ├── __init__.py
│   ├── aggregator.py
│   ├── planner.py
│   ├── reporter.py
│   ├── researcher.py
│   └── summarizer.py
├── config
│   └── settings.py
├── core
│   ├── __init__.py
│   └── llm.py
├── database
│   ├── init_db.py
│   ├── read.py
│   └── write.py
├── tabs
│   ├── chat.py
│   ├── history.py
│   └── swot.py
├── utils
│   └── build_pdf.py
├── .gitignore
├── LICENSE
├── README.md
├── main.py
├── pyproject.toml
├── requirements.txt
└── uv.lock

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🛠 Tech Stack

Layer	Tool
LLM	Gemini API (gemini-3-flash-preview)
Web Search	Serper.dev
Memory	SQLite
Frontend	Streamlit
Package Management	UV
Language	Python 3.12+

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⚙️ Getting Started

Prerequisites

• Python 3.12+
• UV installed
• A Gemini API key
• A Serper.dev API key

Installation

1. Clone the repository

git clone https://github.com/PypCoder/AI-Business-Analyzer.git
cd AI-Business-Analyzer

2. Install dependencies with UV

uv venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
uv pip install -r requirements.txt

3. Set up environment variables

Create a .env file in the root directory:

GEMINI_API_KEY=your_gemini_api_key
SERPER_API_KEY=your_serper_api_key

4. Run the app

streamlit run main.py

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📊 Example Use Case

Input:

"Coffee subscription startup in New York"

Output:

• Executive Summary
• Market & Competitor Landscape
• Key Opportunities
• Risks & Challenges
• Strategic Recommendations
• 30-60-90 Day Action Plan
• Exportable PDF Report
• SWOT Analysis

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🌐 Deployment

This app is deployed on Streamlit Community Cloud.

🔗 Live Demo

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📌 Purpose

This project demonstrates applied AI agent engineering, tool integration, persistent memory handling, and end-to-end system design for business intelligence automation. It serves as a practical example of building production-ready agentic AI systems.

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📄 License

This project is licensed under the MIT License.

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