🏦 AI-Powered Customer Intelligence PoC

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

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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

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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

Phase	Features Used	What It Reveals
1. Behavioral	RFM (Recency, Frequency, Monetary)	How customers transact
2. + Risk	RFM + Credit Score	Who is financially at-risk
3. + Demographics	RFM + Risk + Age + Income	Full customer identity

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

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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             │
└─────────────────────────────────────────────────────┘

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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

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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

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MCP Tools Reference

Tool	What it does
run_sql	Execute any Teradata SQL
list_databases	Show all accessible databases
list_tables	Show tables in a database
get_schema	Column definitions via DBC.ColumnsV
get_table_ddl	Full CREATE TABLE statement
profile_table	Row count, nulls, distinct values, size
check_duplicates	Find duplicate rows on key columns
get_table_stats	Optimizer statistics (COLLECT STATS)
check_data_skew	AMP distribution analysis
get_pi_info	Primary Index definition
find_table_references	Views/macros that use a table
export_to_csv	Save query results to CSV
get_space_usage	Perm space usage per database
run_kmeans_experiment	Elbow analysis for k=2..N
run_kmeans_final	Save final KMeans model to table

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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"

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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

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Tech Stack

Component	Technology
AI Interface	Claude Desktop
AI ↔ Data Protocol	MCP (Model Context Protocol)
Database	Teradata ClearScape Analytics
In-Database ML	Teradata VAL (val.tda_kmeans)
Python ML	teradataml, scikit-learn
Visualization	matplotlib, seaborn

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License

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

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Built with Claude Desktop + Teradata ClearScape Analytics