Think of this as building your own ‘Netflix-style’ recommendation brain: it watches what each user does, learns their tastes, and then uses a mix of traditional recommendation models and modern generative AI to decide what to show or suggest next.
Manually curating content or simple rule-based recommendations (e.g., “most popular”, “newest”) don’t keep users engaged. This solution uses data-driven personalization and GenAI to recommend the right content or products to the right user at the right time, increasing engagement and conversions.
Proprietary behavioral data (clicks, views, purchases, watch time) combined with domain-specific content metadata and tuned recommendation logic; tight integration with existing user experience flows (apps, websites, OTT platforms) creates switching costs and continuous learning loops.
Hybrid
Vector Search
Medium (Integration logic)
Real-time personalization cost and latency at high traffic volumes (online inference in Amazon Personalize plus GenAI calls and vector retrieval).
Early Majority
Combines a managed, battle-tested recommendation engine (Amazon Personalize) with generative AI and vector search to move beyond simple “users like you” lists into richer, more contextual and explainable recommendations tailored to entertainment and similar content-heavy domains.