Media & Entertainment AI Engineering

By surfacing content a viewer actually wants next, balanced with enough diversity to avoid a stale feed. We optimize for measured engagement on your catalog, not just clicks, because a recommender that chases short-term clicks can hurt long-term retention. Honest lift on your audience is the metric that matters, and we measure it with a holdout.

At scale, for clear violations, yes, and it never tires. For nuanced, context-dependent calls it escalates to human moderators rather than guessing, because over-blocking and under-blocking both carry real cost. The design goal is to handle the high-volume obvious cases and route the genuinely hard ones to people, not to remove human judgment.

Only with rights and provenance controls, which we build in. Generative tooling that ignores licensing creates copyright exposure, so we track provenance and respect rights from the start. Used carefully it speeds production; used carelessly it borrows time against a legal claim, and we design for the former.

Usually because it matches titles literally and misses meaning, synonyms, or exact identifiers. We combine meaning-based and keyword search and auto-tag the catalog so assets are findable by what they are, not just what they are called. Missed assets are wasted library value, and it is fixable.

You do. Your catalog, engagement data, trained models, and code remain yours, in your environment. We build on your stack and hand over documentation, so there is no lock-in to us in what we deliver.

There is no single price; cost tracks scope. A single content recommendation build is a modest, weeks-long project, while a wider rollout across your catalog and engagement systems is larger. We scope from one use case, quote a fixed range up front, and sequence so early value funds the next step rather than pricing everything at once.

The return comes from higher engagement, faster production, and lower moderation cost, set against build and running cost. It only holds when the model targets a real, measured cost, so we baseline first and report value against it. We would rather size the return honestly on your numbers than quote an industry average that may not fit you.

A focused pilot on one use case such as content recommendation usually reaches a working version in a few weeks, then tuning on real data. Wider rollout takes longer. We start narrow, prove the numbers, and extend, so you see value early instead of waiting months for one large launch.

Common ones are content recommendations, tagging and search, moderation at scale, and generative creative tooling. The best first project is the one tied to your biggest measurable cost or opportunity, not the most advanced-sounding option. We help you pick the use case where value is fast and the data already supports it.

We start with a short feasibility check on one use case: does the data exist, is it usable, and does it hold the signal the model needs. Often you already have more usable data than you expect. We assess it before recommending any build, so the first step is a decision, not a commitment.