Market Research & Use Case Generation Agent

Project Overview

This project builds a multi-agent system designed to perform comprehensive market research and generate AI use cases tailored for a specified company or industry. The goal is to analyze market standards, assess competitors, and propose AI-driven solutions that align with the company's operational goals and enhance customer experiences.

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Table of Contents

1. High-Level and Low-Level Design
2. Source Code Architecture Overview
3. Installation and Setup
4. Usage
5. Project Structure
6. Contributing
7. License

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High-Level and Low-Level Design

High-Level Design (HLD)

The high-level design provides a broad overview of the application, outlining the main components and data flow:

1. User Interface:
   • Built with Streamlit to capture user input for the company name and industry.
2. Multi-Agent System:
   • A set of modular agents, each handling a specific aspect of the workflow, from industry research to proposal creation.
3. Proposal Generation:
   • Agents produce insights and a final structured document with suggested use cases and relevant resources.

Low-Level Design (LLD)

The low-level design details the responsibilities and functionalities within each agent:

1. Industry Research Agent: Fetches high-level insights on the specified industry.
2. Competitor Analysis Agent: Analyzes competitors’ AI usage within the industry.
3. Use Case Generation Agent: Proposes AI applications aligned with the company's focus areas.
4. Resource Collection Agent: Finds datasets or resources for implementing the use cases.
5. Proposal Creation Agent: Compiles all gathered information into a Markdown document.

For an in-depth look at the High-Level and Low-Level Design, refer to the full document here.

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Source Code Architecture Overview

Objective

The objective of this project is to create a structured, modular system where each agent performs a designated role, contributing to the overall goal of producing a tailored AI use-case proposal.

Components

1. User Interface (UI):

   • File: app.py
   • Role: Takes user input and initiates the process.

2. Agents:

   • Industry Research Agent (industry_research.py): Collects high-level insights into the company’s industry.
   • Competitor Analysis Agent (competitor_analysis.py): Identifies competitors and their AI initiatives.
   • Use Case Generation Agent (use_case_generation.py): Proposes AI/ML use cases based on industry insights.
   • Resource Collection Agent (resource_collection.py): Finds datasets for the proposed use cases.
   • Proposal Creation Agent (proposal_creation.py): Compiles the insights, use cases, and resources into a formatted Markdown proposal.

3. Utility Module (utils.py):

   • Contains helper functions used across different agents.

4. Output:

   • File: output/proposal.md
   • Content: A Markdown file containing a well-structured AI/ML use-case proposal.

Data Flow

1. User Input: User provides the company name and industry in the UI.
2. Industry Research: Insights are generated about the industry.
3. Competitor Analysis: Competitors are analyzed for their AI use.
4. Use Case Generation: Relevant AI use cases are proposed.
5. Resource Collection: Datasets are linked for each use case.
6. Proposal Creation: The final proposal document is generated.

For a more detailed Source Code Architecture Overview, refer to the document here.

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Installation and Setup

1. Clone the Repository

   git clone https://github.com/yourusername/market-research-use-case-generator.git
   cd market-research-use-case-generator

2. Install Dependencies Make sure you have streamlit and other required packages installed. You can install them via:

   pip install -r requirements.txt

3. Run the Application Launch the Streamlit app:

   streamlit run app.py

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Usage

1. Enter Company Information:

   • When prompted, enter the company's name and select its industry from the dropdown.

2. Generate Proposal:

   • The agents will gather insights, suggest use cases, and generate a proposal.md file.

3. Review Proposal:

   • The proposal document can be found in the output/ directory.

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Project Structure

├── app.py                      # Main application file (Streamlit UI)
├── industry_research.py         # Industry research agent
├── competitor_analysis.py       # Competitor analysis agent
├── use_case_generation.py       # Use case generation agent
├── resource_collection.py       # Resource collection agent
├── proposal_creation.py         # Proposal creation agent
├── utils.py                     # Utility functions
├── requirements.txt             # Project dependencies
├── output/
│   └── proposal.md              # Generated proposal file

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Contributing

1. Fork the repository.
2. Create a feature branch (git checkout -b feature/new-feature).
3. Commit your changes (git commit -m 'Add new feature').
4. Push to the branch (git push origin feature/new-feature).
5. Create a new Pull Request.

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License

This project is licensed under the MIT License. See the LICENSE file for more details.

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