Real-time discovery requires models that understand live behavior and infrastructure that can act on it instantly. Traditional collaborative filtering and rules-based approaches fail to capture the complexity of modern user behavior and to react to new information, whether changes in the catalog or in user behavior.
Albatross combines perception models, real-time user and item representations, dynamic ranking, and low-latency serving in one integrated platform.
Albatross relies on sequential embedding models trained on live user-event sequences. These models learn how users respond to products, content, and discovery decisions — then predict the next best items or actions to serve in a user journey, in real time.
As views, clicks, searches, and purchases stream in, Albatross updates its understanding of the user in real time. This keeps predictions aligned with current intent.
LLMs generate content.
Albatross perceives intent
With Albatross, new items do not need to wait days before they can be recommended. The platform connects directly to the catalog and transforms raw item data into rich item representations that capture semantic similarity.
For fast-moving marketplaces, this means fresh inventory can become discoverable within hours of entering the catalog, even when catalog data is messy, incomplete, or inconsistent.
For many businesses, the business goals extend beyond engagement such as purchases. Albatross supports this through a ranking layer designed to optimize for custom business metrics.
The ranking layer combines real-time user-item affinities with catalog attributes and live business context, such as pricing, availability, and promotions.
Beyond perception models, Albatross is a full-stack real-time AI infrastructure. Albatross trains, serves, and optimizes models within a single, integrated pipeline. Albatross unifies capabilities that today require multiple vendors: