Recommendation & Personalisation for Media & Entertainment

Offline accuracy and revenue are different things. We measure recommendations by experiment against the business metric, because that is the only number that pays anyone.

In media & entertainment, rights, attribution and factual accuracy are reputational risks before they are legal ones. That single fact reshapes how recommendation & personalisation has to be built here, the guardrails, the approval points and the evidence trail are design inputs rather than things bolted on before go-live.

The workload we are most often asked to take on first is archive tagging and search, usually integrated against MAM and DAM. We build the smallest thing that proves the case, put it in front of real users, and expand only what earns its keep.

Deployed across regulated and unregulated sectors, with audit trails where the regulator expects them. You own the code, the models where they are open-weight, and the documentation to run it without us.