TasteLab lets product teams test personalization before building costly recommendation infrastructure. Import a catalog, model synthetic audiences, compare strategies, and measure relevance, novelty, diversity, coverage, and filter-bubble risk in one visual lab. Unlike generic AI recommenders, TasteLab uses reproducible simulations, transparent scoring, and experiment-ready reports your team can validate in production.
We built TasteLab because personalization is usually tested too late—after teams have already invested months building recommendation infrastructure. TasteLab lets product teams import a catalog, model synthetic audiences, compare recommendation strategies, and identify trade-offs across relevance, discovery, diversity, coverage, and filter-bubble risk before moving into production. The goal is not to predict real users perfectly. It is to help teams expose weak assumptions earlier, design better experiments, and make more informed product and engineering decisions. We’re launching the first version today and would love feedback from anyone working on recommendations, discovery, personalization, or consumer products.
Report
No reviews yetBe the first to leave a review for TasteLab