CRICINTEL – AI Cricket Engagement Platform

🧠 Inspiration

While watching live cricket on platforms like Disney+ Hotstar, we noticed users often lose interest during ads, slow overs, or breaks. There’s no interaction — just passive watching. We wanted to transform this into an engaging, AI-driven experience that keeps fans hooked throughout the match.

⚙️ What it does

CRICINTEL is an AI-powered cricket engagement platform that enhances live match viewing by providing:

Real-time scoreboard updates AI-generated commentary in multiple styles Interactive quizzes and cricket facts Voice-based commentary

It keeps users engaged even during downtime, turning passive viewing into an interactive experience.

🛠️ How we built it Frontend: React + Material UI for modern UI/UX Backend: Flask for API handling Live Data: Cricbuzz API via RapidAPI AI Engine: Groq LLM for commentary, quiz, and facts Voice: gTTS for text-to-speech

We structured the system into modular components with real-time polling and optimized API usage using caching.

⚠️ Challenges we ran into Handling inconsistent cricket API responses Fixing CORS and backend connectivity issues Dealing with deprecated AI models and switching to supported ones Reducing latency in voice generation Ensuring UI remains responsive with live updates 🏆 Accomplishments that we're proud of Built a fully working real-time AI system Designed a modern, interactive UI with animations Integrated live data + AI + voice seamlessly Created a unique engagement-focused concept Achieved stable performance with fallback handling 📚 What we learned How to integrate real-time APIs with frontend systems Working with LLMs in production-like environments Importance of caching and performance optimization Handling async operations and user experience together Building scalable and modular UI components 🚀 What's next for CRICINTEL – AI Cricket Engagement Platform Add real commentator voice cloning Introduce player stats and analytics dashboard Implement WebSocket for true real-time updates Build a mobile app version Add personalized recommendations based on user behavior

Built With

• cricapi
• flask
• groq
• llm
• react-native
• vite