Test what works
The best model for your data
Continuously test leading recommendation models against your data and deploy the one that performs best.
What it does
The mechanism
- 01
Collaborative, content-based, sequential, graph-based and generative architectures are trained on your actual behaviour and measured against each other on the same footing.
- 02
There is no single architecture that wins on every catalogue, so the platform's job is to find the one that wins on yours and keep checking that it still does.
What it gets you
Three things you can point at
01
Candidates, measured consistently
Every architecture is trained and evaluated the same way, so the comparison between them means something.
02
Optimise for your outcome
Train towards watch time, completion, retention, purchases or any custom event you define, rather than a fixed list of generic signals.
03
One API underneath it all
Architectures, embedding providers and model versions can change without touching your application integration.
04
LLM judgement, bounded
Cross-encoders or listwise LLM rerankers can reorder a bounded candidate set under a strict latency budget, with automatic fallback if the budget is missed - and your deterministic rules still applied afterwards.