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AI-Powered Mentorship Matching System
Developed sophisticated mentorship platform connecting 25,000+ mentors with mentees through AI-powered matching algorithms, real-time messaging, video conferencing, and progress tracking. Achieved 92% successful match satisfaction rate.
Manual mentor-mentee matching process was time-consuming (avg 2 weeks per match), resulted in 40% mismatch rate, and couldn't scale beyond 5,000 users. Existing platform lacked engagement features, resulting in only 35% of matches completing programs.
Designed and implemented AI-powered matching algorithm using collaborative filtering and natural language processing, reducing match time from 2 weeks to instant
Built real-time messaging system with WebRTC video integration, enabling seamless communication between mentors and mentees
Developed comprehensive scheduling system with calendar integration, automated reminders, and timezone handling
Created progress tracking dashboard with goal setting, milestone tracking, and achievement badges to boost engagement
Implemented advanced search and filtering with Elasticsearch for discovering mentors across 50+ skill categories
Creating algorithm that balanced multiple factors: skills, availability, personality, goals, and historical success patterns.
Implemented hybrid approach combining collaborative filtering, content-based filtering, and custom weighting system. Added machine learning component that improved over time based on match outcomes. Achieved 92% satisfaction rate.
Building reliable real-time messaging and video system for thousands of concurrent sessions.
Used Socket.io with Redis adapter for horizontal scaling, implemented WebRTC with TURN servers for NAT traversal, and built fallback mechanisms for poor network conditions. Achieved 99.5% message delivery rate.
Full-text search across 25K profiles with complex filters became slow as data grew.
Migrated to Elasticsearch with optimized indexing strategy, implemented query caching, and added pagination with cursor-based approach. Reduced search time from 3s to 150ms.
Learned to integrate ML models into production systems, including A/B testing different algorithms and monitoring model performance over time.
Gained expertise in WebSocket protocols, WebRTC, and building scalable real-time systems with connection pooling and load balancing.
Improved skills in gathering user feedback, conducting user research, and iterating based on data to improve product-market fit.
Government employee mentorship and career development program
"Hunny built a mentorship platform that transformed our professional development program. The automated matching algorithm and bilingual support were exactly what we needed. The system handles our growing user base efficiently and reliably."