Insurance Customer Migration Analysis POC

A production-grade proof-of-concept demonstrating customer migration analysis using:

• Data Layer: Mock legacy (Excel) + new product (CSV) + competitor data

• MCP Server: Data access tools exposed to CrewAI agents

• CrewAI: Query router + analysis agents for multi-step reasoning

• Gradio UI: Executive-friendly natural language interface

Architecture

Executive Question
    ↓
Gradio UI
    ↓
CrewAI Router Agent (query understanding)
    ↓
CrewAI Analysis Agent (data fetching + insights)
    ↓
MCP Tools (GetRenewedCount, GetLeftCount, etc.)
    ↓
Data Layer (pandas + Excel/CSV queries)
    ↓
Response formatted for executives

Related MCP server: Database MCP Server

Setup

1. Install Dependencies

# Install using uv (recommended)
uv sync

# OR using pip
pip install -r requirements.txt

2. Environment Setup

cp .env.example .env
# Edit .env and add your ANTHROPIC_API_KEY

3. Generate Mock Data

python -m demo_redshift_mcp.data_generator

This creates:

• data/legacy_product.xlsx - 900 legacy customers

• data/new_product_customers.csv - 100 new product customers

• data/competitor_coverage.csv - Competitor history

Running the Application

Web UI (Recommended)

# Launch Gradio interface
python -m demo_redshift_mcp

# Opens at http://localhost:7860

Command Line (Testing)

python -c "
from src.demo_redshift_mcp.crew_agents import run_customer_migration_analysis
result = run_customer_migration_analysis('How many customers renewed?')
print(result)
"

Sample Questions for Executives

1. "How many customers renewed into the new product?"

   • Returns: Count + percentage of migration success

2. "How many customers left and went to competitors?"

   • Returns: Count + breakdown by status (ACTIVE/EXPIRED/CANCELLED)

3. "How many came back from competitors and why?"

   • Returns: Return count + reasons (price, features, etc.)

4. "What's the overall migration summary?"

   • Returns: Comprehensive analysis of all segments

5. "Tell me about customers in California"

   • Returns: State-specific metrics

6. "Who adopted the CONNECTED feature?"

   • Returns: Feature adoption breakdown

Project Structure

demo-redshift-mcp/
├── src/demo_redshift_mcp/
│   ├── app.py                 # Gradio UI entry point
│   ├── crew_agents.py         # CrewAI agents + workflow
│   ├── mcp_server.py          # MCP tools definition
│   ├── data_layer.py          # Data access logic
│   ├── data_generator.py      # Mock data generation
│   └── __init__.py
├── data/                      # Generated mock data
│   ├── legacy_product.xlsx
│   ├── new_product_customers.csv
│   └── competitor_coverage.csv
├── INSURANCE_POC_ARCHITECTURE.md
├── pyproject.toml
└── .env

Key Design Decisions

Data Separation (Excel vs CSV)

• Legacy: Excel (simulates existing systems)

• New: CSV in "Redshift" (simulates cloud OLAP)

• Reason: Tests cross-store join logic early

MCP Over Direct Queries

• Clean abstraction between data and reasoning

• Production-ready: swap CSV with Redshift later

• Agents stay focused on reasoning, not plumbing

Template + Dynamic Fallback

• Fast path: pre-defined queries for common questions

• Flexible path: CrewAI creates logic for edge cases

• Soft inference: combine pricing + feature signals to explain why customers returned

Next Steps

Phase 1: Data ✅

• Mock data generation (900 legacy, 100 new, competitor coverage)

Phase 2: MCP ✅

• Data access tools (GetRenewedCount, GetLeftCount, etc.)

Phase 3: CrewAI ✅

• Query router + analysis agents

Phase 4: Testing

• Run sample questions and verify accuracy

• Test edge cases

Phase 5: UI ✅

• Gradio interface for executives

Future Enhancements

• Replace CSV with actual AWS Redshift

• Add state-level dashboards

• Export reports as PDF/Excel

• Add historical trend analysis

• Deploy as FastAPI endpoint

Troubleshooting

"Module not found" error

# Ensure you're in the right directory
cd /Users/Balu/Documents/Projects/MyCode/demo-redshift-mcp

# Reinstall dependencies
uv sync

"Data not found" error

# Generate mock data
python -m demo_redshift_mcp.data_generator

CrewAI errors

• Ensure ANTHROPIC_API_KEY is set in .env

• Check that you have Claude 3.5 Sonnet (or later) access

Development

Adding a New Query Tool

1. Add method to DataLayer (data_layer.py)

2. Wrap it in InsuranceMCPTools (mcp_server.py)

3. Create CrewAI @tool wrapper (crew_agents.py)

4. Update routing logic in analysis_task

Testing Locally

from src.demo_redshift_mcp.data_layer import DataLayer

data = DataLayer()
result = data.customers_renewed()
print(result)

License

Internal POC - Not for production use without proper data governance.