Transcription and subtitling, semantic search across the archive, tagging and rights-aware metadata, recommendations and moderation support for publishers, broadcasters, podcasters and content companies, with the editor in charge of what is published.
Media companies sit on years of content they cannot find and a daily flow they cannot fully tag. That is a retrieval problem before it is a generation problem.
Transcription and subtitling
Live and archive audio and video transcribed with speaker labels and timing, then aligned for subtitles in multiple languages. Touches the media asset management system and the playout or publishing pipeline. See speech-to-text.
Semantic archive search and clip finding
Find the moment someone said a thing, across decades of content, by meaning rather than filename, with rights filters applied. Touches the archive, the MAM and editorial tools. See semantic search.
Tagging, summaries and metadata
Topics, entities, people, places, summaries and chapter markers generated on ingest and reviewed in bulk, so the CMS and the archive stay consistently described. Touches the CMS and the metadata schema. Better metadata improves search, recommendations and SEO at once.
Recommendations and personalisation
Next article, next episode, next clip, from content embeddings and behaviour, with editorial rules and diversity constraints applied. Touches the app, the website and the analytics warehouse. See recommendation engine.
Comment and user-content moderation support
Risk-ranked triage of comments and submissions, with moderators reviewing the uncertain and the serious first. Touches the community platform and the moderation queue. People make the final call and handle appeals.
Research briefs and audience insight
Briefs assembled from the archive and trusted sources for a journalist to verify, and audience questions answered from analytics in plain language. Touches the archive index and the analytics platform. See AI analytics.
Compliance and data
Rights, editorial standards and audience data
What we design for in media
Content carries licences, territories and contributor rights that any AI reuse must respect, so rights metadata lives in the index and tools enforce it. Editorial standards and accuracy duties mean AI-assisted output is labelled and verified by an editor before publication, and defamation risk makes unverified generation a bad idea. Online-safety duties shape how moderation queues are prioritised and recorded. Audience behaviour used for personalisation is personal data under GDPR and UK GDPR and needs consent and minimisation. This is engineering guidance, not legal advice.
We keep models from training on your archive unless you decide otherwise, run them privately where licensing requires it, store provenance for every generated tag and summary and keep editorial approval in the workflow. Our AI data privacy service covers the audience-data side.
Reference architecture
Flagship workflow: the content intelligence pipeline
New and archived audio, video and text enter the pipeline once. Speech-to-text produces aligned transcripts. An enrichment step generates summaries, chapters, topics and entities and attaches rights and licence metadata from your rights system.
Everything lands in a semantic index with the rights fields as filters. Editorial tools sit on top: archive search, clip finder, research briefs and metadata review. Approved metadata and subtitles are written back to the CMS and MAM; anything destined for publication passes an editor.
The same index later feeds recommendations and audience-facing search. We build on vector search with hybrid retrieval so exact names and dates still match precisely.
Transcribe once, enrich once, index once; every tool and every recommendation reads from the same rights-aware index.
Track record
What we have built that applies
Live session transcription
Eventify transcribes and summarises live conference sessions at scale, with the speaker, noise and vocabulary problems solved in production.
Not in anything we build by default. The high-value work is around the content: transcripts, summaries, tags, archive search, clip finding and research briefs that an editor uses. Where a client wants AI-assisted drafting, the editor owns the piece, the output is labelled and the sources are shown. See AI content operations.
How do you handle rights and licensing?
Rights metadata is part of the index. Every asset carries its licence terms and territory restrictions, and the tools respect them when suggesting reuse. We do not train models on your archive without an explicit decision, and we can run models privately so licensed material never leaves your environment.
How big an archive can semantic search handle?
Millions of items. Audio and video are transcribed once, chunked and embedded; search then runs on the vectors with filters for date, programme, rights and format. The RAG cost estimator gives a feel for storage and processing cost.
Can moderation be automated?
Partly. Models can triage comments and user content by risk and route the uncertain cases to moderators quickly, which is what online-safety duties reward. Final removal decisions and appeals stay with people, and the thresholds are yours to set.
Ready to make AI work in production?
Tell us about your systems, your data and your constraints. We reply within one business day with a straight answer and a suggested next step.