Tamber is a hosted recommendation engine API that lets developers add personalized recommendations to any app in minutes. By streaming user-item interactions in real time, Tamber's machine learning models generate highly accurate recommendations for content, products, and more — ideal for developers and product teams who want Google-scale personalization without building ML infrastructure.
Generate highly accurate personalized content, product, and media recommendations using ML models trained on user behavior
Track user interactions like clicks, purchases, and views as they happen to continuously improve recommendation quality
Choose from Recommended, Next, Weekly, Daily, Popular, Hot, and Trends endpoints to surface content in different contexts
Native client libraries for Node.js, Python, Ruby, Go, Java, iOS (Objective-C), and JavaScript for quick integration
Connect with Twilio Segment for codeless event streaming without writing custom tracking code
Upload up to 1,000 events per API call for efficient historical data import and high-volume tracking
Filter recommendations by item properties, tags, and custom metadata with logical operators for precise targeting
Tag events with page or section context to run experiments and measure recommendation performance across different placements
Merge anonymous and authenticated user profiles to maintain continuous recommendation quality across sessions
Discover trending properties and tags per user to surface higher-level preference insights beyond individual items