Works on a brand-new visitor, runs on one API call, and tells you why it picked every item. No ML team, no black box.
A brand-new visitor has no history to rank, so norai serves popularity for their entity type instead of nothing — then switches to their own affinity as soon as their first events land. Catalog attributes sharpen that very first request even further.
One endpoint to send events, one to ask for recommendations. No feature store to run, no pipeline to babysit, no GPUs, no model to train — the engines are heuristics over your own event stream.
Each item ships with the signal that produced it and a readable detail — affinity, similarity or transitions — so you can settle an argument about why something ranked. Nothing here is a black box you have to take on faith.
Pick an environment once and everything scopes to it. The home screen answers three questions at a glance — is it healthy, is it working, what is it recommending — and the math is always one click down.
{ "data": { "entity_type": "product", "items": [{ "entity_id": "ent_71a3", "score": 0.87, "confidence": 0.72, "reasons": [{ "source": "transitions", "detail": "follows sku-4c19" }] }] }}
Thin clients over the same HTTP API — identity, session batching and retries handled for you. Anything not on this list talks to it directly.
Anything else, ask us directly — you will get an engineer, not a form.
We are starting with a small number of teams. Send us a week of events and we will stand up your environment, wire up the engines, and walk you through the reason behind every recommendation it serves.