Mission: Build AI Recommendation Engine

Personalized recommendations drive 20-40% of engagement and 10-30% of revenue for platforms that implement them well, yet building an effective recommendation engine requires expertise in both ML algorithms and production systems. This mission deploys your AI squad to build a recommendation system combining collaborative filtering, content analysis, and AI embeddings for deeply personalized suggestions. Forge builds the recommendation algorithm with real-time personalization based on user behavior, an A/B testing framework to measure algorithmic quality, and a performant API endpoint for fetching recommendations. The squad implements cold-start handling that delivers relevant suggestions to new users using popularity signals, content attributes, and demographic data. ShipSquad recommendation systems are production-ready from day one, with pre-computed fallbacks for performance, real-time signal processing for freshness, and analytics dashboards that show the direct impact on engagement and revenue metrics. Traditional recommendation projects require dedicated ML teams and months of experimentation. ShipSquad delivers in 3-5 weeks with an A/B testing framework that lets you continuously optimize recommendation quality based on real user behavior.