AI Content Recommendation
Recommendations that lift watch time and retention on your catalog, tuned to your editorial goals instead of a platform's defaults.
The difference between a viewer who stays and one who churns is usually the next thing you show them. We build AI content recommendation systems that get that moment right: models trained on your audience's actual behavior across your catalog, blending collaborative signals with content understanding so new and niche titles surface instead of drowning under the same ten hits. The ranking objective is yours to set, watch time, subscriber retention, catalog breadth, or editorial priorities, and the system handles the cold-start problem for new releases and new users that kills most home-grown recommenders. You own the models and the levers, so your recommendation strategy is not outsourced to a black box that optimizes for someone else's business.
Ingest viewing behavior, catalog metadata, and content embeddings so the models understand both your audience and your library.
Train ranking models against the objective you choose, retention, watch time, breadth, or a weighted blend, not a vendor default.
Serve recommendations in real time across rails, search, and push, with editorial pinning and business rules layered in.
Measure everything with holdout tests, so lift in retention and engagement is proven rather than asserted.
What it does
Hybrid ranking models
Blends collaborative filtering with content understanding, so recommendations work for brand-new titles and long-tail catalog alike.
Tunable objectives
Optimize for retention, watch time, catalog breadth, or editorial goals, with the trade-offs explicit and under your control.
Cold-start handling
New users and new releases get sensible recommendations from day one through content embeddings and fast-adapting models.
Editorial controls
Pins, boosts, and exclusion rules let programming teams shape the experience without fighting the algorithm.
A streaming service lifted average session watch time 14 percent and cut day-30 churn by 9 percent after replacing its rules-based rails.
Questions, answered
Through content understanding: embeddings built from the title's metadata, transcript, and visual features let it be recommended intelligently before any viewing history exists, which is exactly when promotion matters most.
Yes. Pinning, boosting, and exclusion rules are first-class controls, so editorial strategy and the ranking models work together instead of at cross purposes.
Holdout experiments run continuously, comparing the recommender against baselines on retention and engagement. The lift is measured on your audience, not claimed from a case study.
Bring ai content recommendation to your team
Book a free consultation and we'll map the fastest path to production.