Albatross

Founded by Kevin Kahn in 2024

Capture real-time user behavior to personalize product discovery feeds instantly

Cost

Demo

Rating

★ People love it

Time to value

Long Setup (> 1 day)

You can use Albatross to build real-time product discovery experiences for e-commerce and marketplace apps. It tracks every click, search, view, and purchase in-session to understand what a user wants right now — not based on a static profile. It then ranks and recommends the most relevant items from your catalog for each user, updating instantly with every interaction. It handles cold-start for new products, blends promoted and organic content, and scales to billions of predictions per year.

What Albatross does

Track and encode user interaction sequences in real time using transformer modelsRank catalog items by predicted relevance for each individual userOnboard new catalog items into recommendations within minutes of listingBlend promoted items with organic results by relevance scoreServe low-latency item predictions via Prediction API, Catalog API, and Event APIUpdate user embeddings after every click without waiting for batch retrainingScore and return top-K recommended items given a user ID and filtersRun A/B evaluations to measure GMV and engagement impact within 7 weeksUpdates user interest profile on every single click in real timeHandles cold-start: new catalog items become recommendable within minutesProcesses 30+ billion predictions per year at production scaleBlends promoted and organic content by relevance without hurting engagementUses transformer-based perception models trained on live user interaction sequencesServes low-latency recommendations across homepage, product pages, and mood boardsSupports multiple discovery inputs: user ID, item, search query, or candidate listAdapts to catalogs churning at 100,000+ new items per day