Coforge's Data Cosmos Genie AI

Genie AI is Coforge's intelligent, web-based analytics chatbot built on the Cosmos-ML-Flow Airlines MLOps platform powered by Databricks on Azure. It enables business users to explore booking fraud detection, flight delay prediction, and ML model health data through a simple conversational interface — no SQL or data engineering skills required. Part of Coforge Data Cosmos™ — the innovation backbone combining platforms, agentic accelerators, and services for end-to-end data engineering, BI, governance, and analytics.

Core Capabilities: • Natural Language Querying (NLP-to-SQL) – translates plain-English questions into optimized SQL queries, eliminating the need for SQL expertise • 13 Pre-Built Quick Queries – one-click sidebar buttons for common analytics (fraud summary, delay analysis, booking volume, weather impact, model metrics) • Interactive Data Visualization – formatted HTML tables with sticky headers and Chart.js visualizations (bar, line, doughnut) • SQL Transparency – expandable SQL toggle on every response for trust and learning • Dual-Source Badging – responses tagged as 'Genie AI (NLP)' or 'Quick Query' so users know how answers were generated • Session-Based Authentication – secure login/logout with Flask session management • Real-Time & Batch ML Scoring – LightGBM champion and XGBoost challenger models with automated quality gates • Drift Detection & Model Monitoring – continuous tracking of accuracy, precision, recall, F1, and feature drift

Key Value: democratizes analytics for business users, achieves 90%+ fraud detection (vs 60–70% with rule-based systems), enables proactive delay management, and keeps ML models reliable through continuous monitoring.

Key Use Cases: • Banking – conversational fraud analytics and transaction risk exploration for business teams • Insurance – natural language exploration of claims patterns and model health without SQL • Travel – booking fraud detection and flight delay prediction with executive/ops dashboarding • Healthcare – conversational exploration of operational and model performance data for analysts

The 8-week implementation engagement covers: discovery and use-case scoping, platform deployment on Azure Databricks, Genie Space and SQL Warehouse configuration, Quick Query template setup, NLP-to-SQL enablement, visualization and authentication setup, ML pipeline integration, and knowledge transfer.

Target Audience: Business Analysts & Operations Teams, Data Science & ML Engineering Teams, Fraud & Risk Teams, Executive & BI Leaders, and Data Engineering & Platform Teams.