Custora — AI Customer Intelligence Platform

My Role

• 01Led the planning and overall development direction of the Custora AI customer intelligence platform.
• 02Designed the AI system architecture that connects customer data, machine learning models, LLM-based insight generation, and dashboard workflows.
• 03Planned the customer data retrieval flow from CSV upload, Supabase storage, ML inference, and AI-generated retention recommendations.
• 04Initiated the core idea of using AI to support customer retention decisions through churn prediction, sentiment analysis, and recommendation generation.
• 05Coordinated task distribution across frontend, machine learning, backend integration, and documentation.
• 06Built the visual dashboard UI using Next.js and connected the platform with Supabase as the main database.
• 07Prepared the ML model deployment flow using Azure ML and deployed the web application through Vercel.
• 08Advised and supervised ML model development for churn prediction and sentiment analysis.
• 09Assisted backend integration between the dashboard, REST API, ML model service, database, and LLM recommendation flow.
• 10Ensured the platform can transform raw customer data into churn risk insights, customer priority lists, and actionable retention decisions.

Features

• 01Secure dashboard access with login authentication.
• 02CSV Data Hub for uploading customer and review datasets.
• 03Customer churn prediction with churn probability and risk level classification.
• 04Sentiment analysis to understand customer feedback and review signals.
• 05Customer priority list to highlight users that need immediate retention action.
• 06Overview dashboard with KPI cards, charts, customer health summary, and trend visualization.
• 07AI recommendation panel that turns churn and sentiment results into retention action suggestions.
• 08REST API integration connecting the dashboard, database, ML model service, and LLM recommendation flow.

Impact

• Helps businesses detect at-risk customers earlier before they churn.
• Turns raw customer data and feedback into clear business insights.
• Supports data-driven retention decisions through churn risk, sentiment signals, and AI recommendations.
• Reduces manual analysis by combining prediction, visualization, and recommendation in one dashboard.
• Provides an end-to-end AI system, from data upload and ML inference to retention decision support.

Stack