AI-Powered CRM System

Built using the foundational Data_pipeline.sql for customer data cleaning, standardization, and quality flagging. Aligns with the $128.96B AI-Powered CRM market (9.48% CAGR).

Quick Start

Docker Setup (Recommended)

1. Copy env file: cp .env.example .env
2. Start services: docker-compose up --build
3. Access:
   • Dashboard: http://localhost:8501
   • API docs: http://localhost:8000/docs

Local Setup

1. Create .env from .env.example
2. Install dependencies: pip install -r requirements.txt
3. Initialize DB: python src/pipeline_runner.py
4. Start API: uvicorn src.main:app --reload
5. Start Dashboard: streamlit run dashboard/app.py

Pipeline Integration

Leverages all original Data_pipeline.sql logic:

• Data quality checks (email/age/phone/city validation)
• Deduplication by customer_id + email
• Text standardization (INITCAP, TRIM, LOWER)
• Missing value handling (COALESCE)
• Invalid record filtering
• Enrichment (full_name, days_since_registration)
• Data quality flags (Complete/Incomplete) for AI governance

Project Structure

sql/                  # Database schemas and pipeline SQL
src/                  # FastAPI backend + pipeline runner
dashboard/            # Streamlit CRM dashboard

Features

• Automated data pipeline processing
• Customer data quality metrics
• AI-powered basic lead scoring
• RESTful API for CRM data access