🏦 AI-Powered Customer Intelligence PoC

Claude + Teradata ClearScape Analytics via Model Context Protocol (MCP)

What Is This?

This project demonstrates how Claude AI can be connected directly to Teradata to enable:

• 🔍 Natural language data exploration — Ask Claude about your data in plain English

• 🤖 In-database ML — Run KMeans clustering inside Teradata via Claude, no data movement

• 📊 Progressive segmentation — Show how richer features reveal richer customer segments

• 🔗 MCP as a data engineering framework — A reusable pattern for AI + enterprise data

Related MCP server: Teradata MCP Server

The Business Story

"We started with 50,000 customers and 2 years of transactions. By asking Claude natural language questions, we profiled our data, identified quality issues, and ran customer segmentation — all without writing a single line of SQL manually. Everything ran inside Teradata. No data left the platform."

Three Phases of Segmentation

Each phase finds a different optimal number of clusters — proving that richer data = richer insight.

Architecture

┌─────────────────────────────────────────────────────┐
│                  Claude Desktop                      │
│          (Natural Language Interface)                │
└────────────────────┬────────────────────────────────┘
                     │ MCP Protocol
┌────────────────────▼────────────────────────────────┐
│              MCP Server (server.py)                  │
│   13 Tools: query, profile, skew, PI, KMeans...     │
└────────────────────┬────────────────────────────────┘
                     │ teradatasql
┌────────────────────▼────────────────────────────────┐
│         Teradata ClearScape Analytics                │
│   transactions │ demographics │ credit_risk          │
│   segmentation_*_scaled │ val.tda_kmeans             │
└─────────────────────────────────────────────────────┘

Project Structure

teradata-mcp-poc/
│
├── config.example.yaml        ← Copy this → config.yaml and add credentials
├── requirements.txt           ← Python dependencies
│
├── mcp/
│   └── server.py              ← MCP server (15 tools for Teradata)
│
├── notebooks/
│   └── 01_poc_walkthrough.ipynb  ← Full PoC narrative (run this first)
│
├── scripts/
│   └── kmeans_experiment.py   ← Standalone Python experiment runner
│
└── docs/
    └── mcp_tools_reference.md ← All 15 MCP tools explained

Quick Start

Prerequisites

• Python 3.11+

• Claude Desktop (download here)

• Teradata ClearScape account (free trial)

1. Clone the repo

git clone https://github.com/YOUR_USERNAME/teradata-mcp-poc.git
cd teradata-mcp-poc

2. Install dependencies

pip install -r requirements.txt

3. Configure credentials

cp config.example.yaml config.yaml
# Edit config.yaml with your Teradata host, username, and password

4. Connect Claude Desktop to Teradata

Add this to your Claude Desktop config file:

• Mac: ~/Library/Application Support/Claude/claude_desktop_config.json

• Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "teradata": {
      "command": "python",
      "args": ["C:\\path\\to\\teradata-mcp-poc\\mcp\\server.py"]
    }
  }
}

Restart Claude Desktop — you'll see teradata appear in Settings → Connectors.

5. Talk to your data

Open Claude Desktop and ask:

List all my Teradata databases
Show me the tables in demo_user
Profile the transactions table
Check for data skew on the customer_demographics table
Run a data quality check on demo_user.credit_risk

6. Run the notebook

jupyter notebook notebooks/01_poc_walkthrough.ipynb

MCP Tools Reference

Example Conversations with Claude

Data exploration:

"Profile the transactions table and tell me if there are any data quality issues"

Segmentation:

"Run a KMeans experiment on segmentation_rfm_scaled using monetary_scaled, frequency_scaled, recency_scaled for k=2 to 8"

Performance:

"Check if the customer_demographics table has any skew issues and review its Primary Index"

Lineage:

"Find everything that references the transactions table"

Dataset

The PoC uses a synthetic customer dataset with:

• 799,477 transactions across 50,000 customers (Jan 2023 – Dec 2024)

• Customer demographics: age, income, employment years

• Credit risk: credit score (400–849), loan exposure, late payments

• Pre-built features: RFM aggregations, multiple scaled tables for clustering

Tech Stack

License

MIT — free to use and adapt for your own PoC.

Built with Claude Desktop + Teradata ClearScape Analytics