Generative AI: Media and Entertainment

Generative AI for media and entertainment

Media companies sit on huge archives and produce content against relentless deadlines, but generative AI raises questions no other industry feels as sharply: who owns the output, whose voice or face is in it, and can the audience tell what is real. We build AI that makes your own content easier to find, tag, localise and license, keeps editors in charge of what is published, and records provenance and consent along the way. Senior engineers own the design, AI coding agents carry the volume, and every line is reviewed by a human.

Discuss a media AI use case Media and entertainment software

Key highlights

We build archive search, metadata and highlights automation, localisation pipelines, rights extraction and newsroom copilots for media companies.
Most value comes from AI that understands your existing content, not AI that generates new faces or voices.
Any synthetic voice or likeness use is tied to recorded consent and contract terms, and outputs carry C2PA Content Credentials.
Editors approve everything that is published, and every AI contribution is logged with the model and sources used.
A first use case, such as semantic search over one archive collection, usually reaches a guarded pilot in 8 to 12 weeks.

Why AI projects stall in media and entertainment

The technology works. Projects stall on legal review, union and talent agreements, and editorial teams who will not put their name to output they cannot verify.

Copyright uncertainty on both sides. Lawyers worry about models trained on unlicensed work and about whether AI generated material can be protected at all. The US Copyright Office has said purely AI generated material is not copyrightable without sufficient human authorship.

Likeness, voice and consent. Talent and union agreements, such as the SAG-AFTRA contracts that followed the 2023 strikes, require informed consent and compensation for digital replicas. A tool that cannot track consent cannot be used.

Archives nobody can search. Decades of footage, photos and audio are described by a few inconsistent keywords. Producers and researchers rebuy stock or reshoot because finding the right clip takes too long.

Metadata and compliance work by hand. Logging, tagging, content warnings, age rating notes and ad break markers are created manually for every asset and every market, and they are the first thing cut when deadlines slip.

Rights locked in contracts. Which territories, windows and platforms a title can run on is buried in PDFs and spreadsheets. Sales and scheduling teams wait days for answers from rights staff.

Trust in a synthetic media era. Audiences and platforms increasingly ask whether an image or clip is authentic. Newsrooms need to prove what they captured and disclose what AI touched.

Use cases

Generative AI use cases we build for media and entertainment

Most of these work on content you already own. Generation is used where consent, rights and editorial review are in place.

Multimodal archive search

Transcription, scene and shot detection, face and logo recognition where licensed, and multimodal embeddings let researchers search footage, photos and audio in plain language and jump straight to the right timecode.

Automated metadata and highlights

Pipelines generate descriptions, keywords, chapters, content warnings and ad break suggestions in your MAM, and propose highlight clips from sports or live events for an editor to approve.

Localisation and subtitling

Speech to text, translation and subtitle timing for many languages, with glossaries for names and terms and a human linguist in review. Synthetic dubbing is offered only where voice consent and contracts allow it.

Rights and contract extraction

Document extraction turns licensing and talent contracts into structured rights: territories, windows, platforms, exclusivity and AI use clauses, so a rights query is answered from data and checked by your rights team.

Newsroom and editorial copilot

RAG over your published archive, wire feeds and style guide to draft summaries, headlines, SEO metadata and social copy, with sources linked and an editor approving every published word.

Audience and subscriber support agent

A tool using agent that reads subscription, billing and entitlement data to answer account questions, fix playback issues with guided steps and retain cancelling subscribers with approved offers, handing over to staff when needed.

How we deliver

From one collection to the whole catalogue

01
Use case discovery and data readiness
We rank use cases with editorial, legal and technology teams, then check the material: formats, existing metadata, transcripts, rights records and which content may be processed by which model under your contracts.
02
Evaluation harness and prototype
Archivists and editors label a golden set of searches, tags or translations. The prototype is scored on relevance, accuracy and house style on every change, so quality is measured before anyone relies on it.
03
Guarded pilot with editors in the loop
One desk, one collection or one language goes live. Editors accept, correct or reject every output, and those decisions feed the test set.
04
Production rollout with monitoring
Scaling to the full catalogue and more teams, with processing cost tracking, quality dashboards, drift alerts and a log of every AI contribution for legal and standards teams.

AI governance for media and entertainment

In media, governance is about rights as much as data. We design for copyright, consent and provenance from the first release.

C2PA Content Credentials Talent consent tracking SAG-AFTRA and union terms Copyright and licensing review EU AI Act deepfake disclosure GDPR and UK GDPR Editorial standards logging Data residency by region Self hosted model option

Consent and rights are checked before generation. Any use of a performer's voice or likeness is tied to a consent record and the contract terms that allow it, and the system refuses jobs without one. Licensed content is processed only by models and vendors your agreements permit, including self hosted open weight models when content cannot leave your cloud.

Provenance travels with the content. We attach C2PA Content Credentials to assets so the capture source and any AI edits are recorded in signed metadata. From August 2026, EU AI Act transparency rules require deepfakes and AI generated text published to inform the public on matters of public interest to be disclosed, with an exception for text that has had human editorial review.

Editors stay accountable. Every AI generated description, translation or draft records the model, prompt, sources and approving editor. Nothing is published automatically, and standards teams can audit what AI contributed to any story or asset.

Models and tooling we use for media AI

Model choice is made per use case after testing on your content. The pipeline and integrations are what make it work at catalogue scale.

Anthropic Claude OpenAI GPT models Google Gemini multimodal Whisper speech to text Llama and Mistral self hosted CLIP and multimodal embeddings FFmpeg media pipelines C2PA SDK and Content Credentials MAM and DAM integration pgvector and Elasticsearch LangGraph agents Langfuse tracing and evals

FAQ

Common questions

Can we use generative AI without copyright risk?

You can reduce it a great deal. We focus on AI that works on content you already own or license, check that your contracts permit the processing, prefer models with clear enterprise terms, and keep humans meaningfully involved in creative output. Specific rights questions should still be reviewed by your legal team.

How do you handle voice and likeness consent?

Consent is a hard requirement in the system, not a policy document. Any synthetic voice or likeness job must reference a recorded consent and the contract terms that cover it, or it is refused. Uses are logged per performer so your talent team can report on them.

How accurate are AI metadata and translations?

Accurate enough to remove most manual first pass work, not accurate enough to publish unchecked. We measure accuracy on a labelled set from your own archive before pilot, set thresholds with your editors, and route low confidence items to a person.

How long does a media AI project take?

Usually 8 to 12 weeks to a guarded pilot for one use case. That covers discovery, a labelled test set, the processing pipeline and a pilot on one collection or desk. Catalogue scale processing follows once quality is confirmed.

Should we buy a media AI tool or build our own?

Buy for commodity tasks such as basic transcription if the vendor's terms suit your content. Build when you need your own archive, rights data, style guide and MAM in one workflow, or when you are a platform shipping AI features to your own audience. We often combine both.

How is a media AI project priced?

Usually as a fixed price discovery and prototype phase, then a pilot and rollout scoped from measured results. Processing costs depend on hours of content, languages and model choice, and we estimate them per use case before you commit. See our pricing page for engagement models.

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