NSL-1: A RECOMMENDER YOU CAN SEE INSIDE

Users, content, interests and behavioural clusters in one space you can actually look at. The communities in your data are found rather than defined, so the groups you did not know to look for turn up beside the ones you did.

Follow one person's interests as they move, and tell a settled taste apart from a single unusual session. Fragmented signals become one current picture of what somebody wants now - not an average of everything they have ever done.

Multimodal embeddings - TwelveLabs' Marengo, OpenAI, or your own - let ranking read scenes, actions, dialogue, sound, objects and themes rather than titles and tags. A new item can be placed the day it is published, with no interaction history at all.

Collaborative, content-based, sequential, graph and generative architectures, trained on your actual behaviour and measured against each other on the same footing. Towards your outcome - watch time, completion, retention, purchases - not a fixed list of generic signals.

A candidate only goes live when it beats the incumbent on the agreed metrics and stays inside your quality, diversity, latency and cost guardrails. Shadow first, then a canary on a slice of traffic, with automatic rollback if the live numbers move the wrong way.

Boost, suppress, exclude, pin or guarantee content, and set how hard an editorial decision pushes against personalisation. Every result traces back to the interests, interactions, similarities and rules that produced it.