Discovery and moderation at library scale.

Most catalogues have a long tail nobody has watched because nobody has been shown it — and metadata too thin for anyone to find it deliberately.

01 / What is hard here

The problem underneath the problem.

Recommendation in media has a strong feedback loop: the system promotes what performed, which is what was promoted. Left unexamined it narrows the catalogue, concentrates viewing on a small slice, and reports this as an improvement in engagement.

Moderation has the opposite problem. Volume makes human review impossible, automation makes context-dependent errors, and both over-removal and under-removal carry real cost — in a market with several languages, scripts and regional sensitivities in the same feed.

02 / What we build

Work we take on in media & entertainment.

Catalogue enrichment: Automatic tagging, scene and topic detection, subtitle alignment and metadata generation that makes the long tail findable at all.
Discovery and ranking: Two-stage recommendation with deliberate exploration budget and catalogue-coverage monitoring, so that the system does not quietly shrink your library.
Content moderation support: Multi-language classification with confidence thresholds, human review queues and a measured appeal path — designed as triage rather than replacement.
Audience analytics: Cohort behaviour, retention and content performance analysis at title and segment level.
Localisation workflow: Subtitle and dub pipeline support across Indian languages with human review at the quality gate.

03 / Constraints

What shapes the build.

These are design constraints agreed at the start, not compliance work bolted on before launch. Retrofitting them is expensive and usually incomplete.

Catalogue coverage: Monitored as a first-class metric. A recommender that only surfaces the top few hundred titles is making a merchandising decision nobody approved.
Rights and windowing: Availability, territory and licensing constraints applied as a hard layer above ranking, not learned from data.
Moderation appeals: Automated removals need a review route, and appeal outcomes feed back into evaluation rather than disappearing.
Regional context: Language, script and regional sensitivity vary enough that a single national moderation model will be wrong in predictable places.

04 / What we advise against

Where we would say no.

We would advise against fully automated content removal without an appeal path, and against optimising recommendation on engagement alone — it is the metric most reliably improved by making the product worse.

We would rather lose the work than build something that creates a liability for you or a harm for the people on the other side of it. If that rules out what you had in mind, it is better established now than at delivery.

05 / Other sectors

Adjacent work.

Different domains, the same delivery discipline — a measured baseline, a real evaluation set, and monitoring that catches decay early.

Next step

Working in media & entertainment?

Tell us which process is slow, expensive or error-prone. The diagnostic will tell you whether this is a problem worth solving with machine learning — including if it is not.

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