AI for Media & Entertainment
Media businesses do not have an AI problem, they have a catalogue problem. Thousands of assets published daily, metadata written by whoever was on shift, and an archive nobody can search by meaning. That is the shape of the work we do here.
Why content operations stall before AI does
Editorial teams are asked to connect formats by hand: this article to that clip, this clip to that archive segment. It works at ten items a day and collapses at a thousand. The usual first fix is keyword matching, which fails immediately, because a video titled for a social feed shares almost no vocabulary with the article it belongs to. The second fix is more metadata discipline, which fails because it asks an editorial team already at capacity to do more clerical work. Neither is an AI problem yet. The AI problem shows up one layer down: retrieval that understands meaning rather than wording, running continuously as new content lands, cheap enough per item that it can run on everything rather than on a curated subset.
What AI actually does in media & entertainment.
The four workloads that carry the value in this industry, in the order they usually pay off.
Semantic content matching
Embed articles and video captions into the same vector space and let similarity, not keywords, decide what belongs together. This is what we built for Fanziz: captions extracted from every uploaded video, articles embedded the same way, and a vector similarity search that surfaces the right clip alongside an article the moment it publishes.
Archive search that understands meaning
The same embedding layer turns a dormant archive into a queryable asset. Editors ask for the idea rather than guessing the headline someone wrote in 2019, and rights and usage metadata rides along with the result so a clip is never surfaced that cannot legally be used.
Recommendation and content enrichment
Once relationships between assets are computed rather than curated, related-content rails, topic pages and personalised feeds stop being manual editorial products. New content is enriched in the background as it arrives, which is the only version of this that survives daily publishing volume.
Inference cost control at publishing scale
Running any of the above across a full catalogue is where budgets break. Retrieval that pulls only the passages a request needs, caching for context reused across many requests, and routing so lightweight NLP handles classification and entity extraction while the expensive model is reserved for genuine reasoning. We rebuilt Fanziz's whole inference pipeline on exactly these lines.
Provenance and disclosure are now product requirements
Since 2 August 2026, Article 50 of the EU AI Act requires that AI-generated or AI-manipulated audio, image, video and text be marked in a machine-readable format and made detectable as AI-generated, with a reinforced disclosure obligation on deepfakes. For a media platform that is not a legal footnote, it is a schema decision: provenance has to be a field your CMS carries from generation through publication, because retrofitting it across a live catalogue is far more expensive than designing for it. Rights and licensing metadata deserve the same treatment. We scope both at design time, and we keep recommendation and matching systems auditable so an editor can always see why two assets were linked.
The practice areas this work draws on.
Published work in media & entertainment.
Media & Entertainment questions, answered.
No, and we would advise against it. The matching and enrichment layer reads from your CMS and writes associations back to it. Fanziz kept their entire publishing stack; what changed was a background pipeline that embeds new content as it lands and a vector index alongside it.
That is the case keyword matching cannot solve and semantic matching is built for. We embed the video's captions rather than its title, so the comparison happens on what the video is actually about. Divergent wording between an article and its matching clip stops being a problem.
Backfill is a one-off cost you can size precisely before committing, and it is usually far smaller than teams expect because embedding is cheap relative to generation. The recurring cost is the ongoing trickle of new content, which is where the retrieval, caching and routing work matters most.
We make sure the technical facts are captured: what was generated, by which model, when, and in a machine-readable form that survives your publishing pipeline. Formal interpretation of your obligations is a conversation for your counsel, and we will give them accurate documentation to work from.