AI-Powered Market Research Platform Development by Raj PrajapatiAI-Powered Market Research Platform Development by Raj Prajapati

AI-Powered Market Research Platform Development

Raj Prajapati

Completed work

AI Agent Designer

Prompt Writer

Service Designer

LangChain

NLTK

pandas

Artificial Intelligence

Market Research AI Platform

A comprehensive, AI-powered market research platform that combines specialized agents to deliver in-depth analysis of markets, competitors, consumer sentiment, and industry trends.

🌟 Features

Multi-Agent Architecture: Specialized AI agents working together to provide comprehensive market insights

Competitive Analysis: Detailed analysis of competitors, their strengths, weaknesses, and market positioning

Consumer Insights: Analysis of consumer sentiment, preferences, and behavior patterns

Market Sizing: Data-driven market size estimation and growth projections

Trend Identification: Recognition of emerging market trends and developments

SWOT Analysis: Automated SWOT analysis for companies based on real data

Interactive UI: User-friendly Streamlit interface for easy interaction

📊 Key Capabilities

Web Data Integration: Extracts and analyzes information from multiple web sources

API Integration: Connects with external APIs for enhanced data collection (News API, Google Maps, SerpAPI)

Sentiment Analysis: Uses NLP to analyze consumer sentiment about products and services

Market Estimation: Provides data-driven market size estimates with regional breakdowns

Automated Research: Conducts complex market research tasks with minimal user input

🏗️ Architecture

The platform uses a hierarchical agent structure:

Supervisor
├── Competitive Analysis Agent
├── Consumer Insights Agent
├── Market Sizing Agent
└── Market Trends Agent

Supervisor: Coordinates specialized agents and synthesizes their findings

Competitive Analysis Agent: Analyzes competitive landscape using real data

Consumer Insights Agent: Extracts and analyzes consumer sentiment and behavior

Market Sizing Agent: Estimates market size, growth rates, and projections

Market Trends Agent: Identifies current and emerging trends in the market

📂 Project Structure

market_research/
├── tools/                      # Tool implementations
│   ├── __init__.py
│   └── agent_tools.py          # Implementation of research tools
├── app.py                      # Entry point for running the application
├── agent_definations.py        # All agents definations
├── supervisor.py               # Agent orchestration and coordination
├── market_research_app.py      # Streamlit UI implementation
├── requirements.txt            # Project dependencies
└── README.md                   # This file

🚀 Getting Started

Prerequisites

Python 3.8+

OpenAI API key

(Optional) News API key

(Optional) Google Maps API key

(Optional) SerpAPI key

Installation

Clone the repository:

git clone https://github.com/yourusername/market-research-ai.git
cd market-research-ai

Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Install dependencies:

pip install -r requirements.txt

Create a .env file in the project root and add your API keys:

OPENAI_API_KEY=your_openai_api_key
NEWS_API_KEY=your_news_api_key
GOOGLE_MAPS_API_KEY=your_google_maps_api_key
SERP_API_KEY=your_serp_api_key

Running the Application

Launch the Streamlit web interface:

streamlit run market_research_app.py

The web interface will be available at http://localhost:8501.

🔧 Usage Examples

Basic Market Research

To conduct a basic market research:

from supervisor import Supervisor

supervisor = Supervisor()
query = "What is the market size for electric vehicles in Europe?"
response = supervisor.run(query)
print(response)

Comprehensive Market Analysis

For a more detailed analysis with specific components:

query = """
Analyze the fitness app market with focus on:
1. Key competitors and their strengths
2. Current market size and growth projections
3. Consumer sentiment about existing solutions
4. Emerging trends in the industry
"""
response = supervisor.run(query)

Localized Market Research

For location-specific market research:

query = "I want to start an IT training center in Ahmedabad, what is the market size and how to start it?"
response = supervisor.run(query)

📊 Sample Outputs

Market Size Analysis

# Market Size Analysis: IT Training in Ahmedabad

## Current Market Size
**$45.2 million**

## Annual Growth Rate (CAGR)
**12.5%**

## Projected Market Size (5 Years)
**$81.4 million**

## Key Market Drivers
- Rising demand for technical skills in the IT sector
- Growth of IT and ITES companies in Gujarat
- Government initiatives promoting digital literacy
- Increasing adoption of cloud and AI technologies

## Major Market Challenges
- Intense competition from established training centers
- Rapidly changing technology landscape requiring frequent curriculum updates
- Price sensitivity in the local market

## Leading Companies
- NIIT
- Jetking
- Aptech Computer Education
- Seed Infotech
- AkashTechnoLabs

Competitive Analysis

# Competitive Analysis: Coffee Shop XYZ vs. Competitors

## Coffee Shop XYZ
A specialty coffee shop focusing on premium single-origin beans and artisanal brewing methods.

## Competitor Profiles

### Starbucks
**Competitive Position**: Starbucks is significantly larger with global brand recognition but offers a more standardized experience compared to Coffee Shop XYZ.

### Blue Bottle Coffee
**Competitive Position**: Blue Bottle operates in a similar premium segment with comparable quality but has stronger brand recognition and more locations.

### Philz Coffee
**Competitive Position**: Philz has a strong local presence with a loyal customer base and unique pour-over coffee approach.

## SWOT Analysis for Coffee Shop XYZ

### Strengths
- Specialty coffee expertise with high-quality beans
- Personalized customer experience
- Unique brewing methods not available at chain stores
- Strong relationships with ethical bean suppliers

### Weaknesses
- Limited physical presence
- Higher price points than mainstream competitors
- Lower brand recognition
- Limited marketing budget

### Opportunities
- Growing consumer interest in specialty coffee
- Potential for coffee subscription service
- Educational workshops on coffee brewing
- Partnerships with local businesses

### Threats
- Expansion of premium coffee chains into the local market
- Economic downturns affecting discretionary spending
- Rising costs of quality coffee beans
- Increasing competition in the specialty coffee segment

📚 API Documentation

Supervisor

The Supervisor class coordinates multiple specialized agents to provide comprehensive market research.

supervisor = Supervisor()
response = supervisor.run(query)

Specialized Agents

The platform includes the following specialized agents:

competitive_analysis_agent: Analyzes competitive positioning

consumer_insights_agent: Analyzes consumer behavior and sentiment

market_sizing_agent: Estimates market size and growth

market_trends_agent: Identifies emerging trends

Research Tools

The platform provides various research tools:

search_news: Searches for recent news articles

analyze_competitors: Analyzes competitive landscape

consumer_sentiment_analysis: Analyzes sentiment about products/services

market_size_estimation: Estimates market size and growth

swot_analysis: Performs SWOT analysis for companies

📋 Dependencies

Major dependencies include:

langchain: For creating and managing AI agents

openai: For accessing GPT-4o and other OpenAI models

streamlit: For the web interface

nltk: For sentiment analysis

beautifulsoup4: For web scraping

matplotlib: For data visualization

pandas: For data manipulation

Optional dependencies:

yfinance: For financial data on public companies

googlemaps: For local business data

serpapi: For enhanced web search

⚠️ Troubleshooting

API Key Issues

If you encounter errors related to API keys:

Verify that your .env file contains the correct API keys

Ensure the environment variables are being loaded properly

Check that your API keys are active and have sufficient quota

Rate Limiting

If you encounter rate limiting issues:

Reduce the number of consecutive requests

Implement retry logic with exponential backoff

Consider upgrading your API plans for higher rate limits

Missing Dependencies

If you encounter missing dependencies:

pip install -r requirements.txt

For optional dependencies:

pip install yfinance googlemaps google-search-results

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Fork the repository

Create your feature branch (git checkout -b feature/amazing-feature)

Commit your changes (git commit -m 'Add some amazing feature')

Push to the branch (git push origin feature/amazing-feature)

Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgements

OpenAI for GPT-4o

LangChain for the agent framework

All the data providers and API services that make this platform possible

Like this project

Completed work

Posted Jul 12, 2025

Developed an AI-powered market research platform with specialized agents for in-depth analysis.

Likes

Views

Timeline

Jun 16, 2025 - Jul 7, 2025

AI-Powered Market Research Platform Development

Raj Prajapati

Completed work

AI Agent Designer

Prompt Writer

Service Designer

LangChain

NLTK

pandas

Artificial Intelligence

Market Research AI Platform

A comprehensive, AI-powered market research platform that combines specialized agents to deliver in-depth analysis of markets, competitors, consumer sentiment, and industry trends.

🌟 Features

Multi-Agent Architecture: Specialized AI agents working together to provide comprehensive market insights

Competitive Analysis: Detailed analysis of competitors, their strengths, weaknesses, and market positioning

Consumer Insights: Analysis of consumer sentiment, preferences, and behavior patterns

Market Sizing: Data-driven market size estimation and growth projections

Trend Identification: Recognition of emerging market trends and developments

SWOT Analysis: Automated SWOT analysis for companies based on real data

Interactive UI: User-friendly Streamlit interface for easy interaction

📊 Key Capabilities

Web Data Integration: Extracts and analyzes information from multiple web sources

API Integration: Connects with external APIs for enhanced data collection (News API, Google Maps, SerpAPI)

Sentiment Analysis: Uses NLP to analyze consumer sentiment about products and services

Market Estimation: Provides data-driven market size estimates with regional breakdowns

Automated Research: Conducts complex market research tasks with minimal user input

🏗️ Architecture

The platform uses a hierarchical agent structure:

Supervisor
├── Competitive Analysis Agent
├── Consumer Insights Agent
├── Market Sizing Agent
└── Market Trends Agent

Supervisor: Coordinates specialized agents and synthesizes their findings

Competitive Analysis Agent: Analyzes competitive landscape using real data

Consumer Insights Agent: Extracts and analyzes consumer sentiment and behavior

Market Sizing Agent: Estimates market size, growth rates, and projections

Market Trends Agent: Identifies current and emerging trends in the market

📂 Project Structure

market_research/
├── tools/                      # Tool implementations
│   ├── __init__.py
│   └── agent_tools.py          # Implementation of research tools
├── app.py                      # Entry point for running the application
├── agent_definations.py        # All agents definations
├── supervisor.py               # Agent orchestration and coordination
├── market_research_app.py      # Streamlit UI implementation
├── requirements.txt            # Project dependencies
└── README.md                   # This file

🚀 Getting Started

Prerequisites

Python 3.8+

OpenAI API key

(Optional) News API key

(Optional) Google Maps API key

(Optional) SerpAPI key

Installation

Clone the repository:

git clone https://github.com/yourusername/market-research-ai.git
cd market-research-ai

Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

Install dependencies:

pip install -r requirements.txt

Create a .env file in the project root and add your API keys:

OPENAI_API_KEY=your_openai_api_key
NEWS_API_KEY=your_news_api_key
GOOGLE_MAPS_API_KEY=your_google_maps_api_key
SERP_API_KEY=your_serp_api_key

Running the Application

Launch the Streamlit web interface:

streamlit run market_research_app.py

The web interface will be available at http://localhost:8501.

🔧 Usage Examples

Basic Market Research

To conduct a basic market research:

from supervisor import Supervisor

supervisor = Supervisor()
query = "What is the market size for electric vehicles in Europe?"
response = supervisor.run(query)
print(response)

Comprehensive Market Analysis

For a more detailed analysis with specific components:

query = """
Analyze the fitness app market with focus on:
1. Key competitors and their strengths
2. Current market size and growth projections
3. Consumer sentiment about existing solutions
4. Emerging trends in the industry
"""
response = supervisor.run(query)

Localized Market Research

For location-specific market research:

query = "I want to start an IT training center in Ahmedabad, what is the market size and how to start it?"
response = supervisor.run(query)

📊 Sample Outputs

Market Size Analysis

# Market Size Analysis: IT Training in Ahmedabad

## Current Market Size
**$45.2 million**

## Annual Growth Rate (CAGR)
**12.5%**

## Projected Market Size (5 Years)
**$81.4 million**

## Key Market Drivers
- Rising demand for technical skills in the IT sector
- Growth of IT and ITES companies in Gujarat
- Government initiatives promoting digital literacy
- Increasing adoption of cloud and AI technologies

## Major Market Challenges
- Intense competition from established training centers
- Rapidly changing technology landscape requiring frequent curriculum updates
- Price sensitivity in the local market

## Leading Companies
- NIIT
- Jetking
- Aptech Computer Education
- Seed Infotech
- AkashTechnoLabs

Competitive Analysis

# Competitive Analysis: Coffee Shop XYZ vs. Competitors

## Coffee Shop XYZ
A specialty coffee shop focusing on premium single-origin beans and artisanal brewing methods.

## Competitor Profiles

### Starbucks
**Competitive Position**: Starbucks is significantly larger with global brand recognition but offers a more standardized experience compared to Coffee Shop XYZ.

### Blue Bottle Coffee
**Competitive Position**: Blue Bottle operates in a similar premium segment with comparable quality but has stronger brand recognition and more locations.

### Philz Coffee
**Competitive Position**: Philz has a strong local presence with a loyal customer base and unique pour-over coffee approach.

## SWOT Analysis for Coffee Shop XYZ

### Strengths
- Specialty coffee expertise with high-quality beans
- Personalized customer experience
- Unique brewing methods not available at chain stores
- Strong relationships with ethical bean suppliers

### Weaknesses
- Limited physical presence
- Higher price points than mainstream competitors
- Lower brand recognition
- Limited marketing budget

### Opportunities
- Growing consumer interest in specialty coffee
- Potential for coffee subscription service
- Educational workshops on coffee brewing
- Partnerships with local businesses

### Threats
- Expansion of premium coffee chains into the local market
- Economic downturns affecting discretionary spending
- Rising costs of quality coffee beans
- Increasing competition in the specialty coffee segment

📚 API Documentation

Supervisor

The Supervisor class coordinates multiple specialized agents to provide comprehensive market research.

supervisor = Supervisor()
response = supervisor.run(query)

Specialized Agents

The platform includes the following specialized agents:

competitive_analysis_agent: Analyzes competitive positioning

consumer_insights_agent: Analyzes consumer behavior and sentiment

market_sizing_agent: Estimates market size and growth

market_trends_agent: Identifies emerging trends

Research Tools

The platform provides various research tools:

search_news: Searches for recent news articles

analyze_competitors: Analyzes competitive landscape

consumer_sentiment_analysis: Analyzes sentiment about products/services

market_size_estimation: Estimates market size and growth

swot_analysis: Performs SWOT analysis for companies

📋 Dependencies

Major dependencies include:

langchain: For creating and managing AI agents

openai: For accessing GPT-4o and other OpenAI models

streamlit: For the web interface

nltk: For sentiment analysis

beautifulsoup4: For web scraping

matplotlib: For data visualization

pandas: For data manipulation

Optional dependencies:

yfinance: For financial data on public companies

googlemaps: For local business data

serpapi: For enhanced web search

⚠️ Troubleshooting

API Key Issues

If you encounter errors related to API keys:

Verify that your .env file contains the correct API keys

Ensure the environment variables are being loaded properly

Check that your API keys are active and have sufficient quota

Rate Limiting

If you encounter rate limiting issues:

Reduce the number of consecutive requests

Implement retry logic with exponential backoff

Consider upgrading your API plans for higher rate limits

Missing Dependencies

If you encounter missing dependencies:

pip install -r requirements.txt

For optional dependencies:

pip install yfinance googlemaps google-search-results

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Fork the repository

Create your feature branch (git checkout -b feature/amazing-feature)

Commit your changes (git commit -m 'Add some amazing feature')

Push to the branch (git push origin feature/amazing-feature)

Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgements

OpenAI for GPT-4o

LangChain for the agent framework

All the data providers and API services that make this platform possible

Like this project

Completed work

Posted Jul 12, 2025

Developed an AI-powered market research platform with specialized agents for in-depth analysis.

Likes

Views

Timeline

Jun 16, 2025 - Jul 7, 2025