AI for
Media
Years of footage, many platforms, and advertisers who want proof it worked. We build the tagging, search, and analytics systems that make a content library earn its keep.
The real problems
What slows a content operation down.
Content ops at scale
New content arrives constantly, and each piece needs metadata, captions, and a version for every platform. Manual teams fall behind fast.
Archive discoverability
Old footage indexed by filename and tape label. Producers know the shot exists somewhere. Finding it takes far longer than it should.
Ad-revenue attribution
Ad spend spread across broadcast, streaming, and social with no shared way to measure it. Which platform actually earned the revenue stays a guess.
Multi-platform distribution
Every piece gets cut, reformatted, and re-captioned for each platform by hand. The same show ends up shipped several different ways.
Our solutions
AI for the content pipeline.
Content tagging & metadata
Vision and language models that tag people, places, topics, and moments automatically as content comes in, no manual logging needed.
Plain-language archive search
Describe the shot you need in ordinary words. Search across transcripts, tags, and visual features returns the clip, not a list of near-misses.
Audience analytics
Viewing behaviour unified across broadcast and digital, so content decisions and ad pricing are backed by one clear picture, not two conflicting ones.
Automated compliance review for broadcast
Models that pre-screen content against broadcast codes, flagging language, claims, and restricted material before it reaches human review.
How we engage
From ingest to insight.
Library & workflow audit
We map your ingest, MAM, and distribution workflows, and check archive quality before proposing any pipeline.
Pilot on one collection
We start with one programme archive or one channel and measure tagging accuracy against your own editors' judgement.
Validate & expand
Producers and researchers review search quality with us. Once it holds up, indexing expands collection by collection.
Sustain & retrain
Models get tuned as house style and platforms change. Your media ops team runs the pipeline day to day.
Common questions
Media AI, answered directly.
Can AI actually search our archive using plain descriptions instead of exact tags?
Yes - plain-language archive search works across transcripts, tags and visual features, so a producer can describe a shot in ordinary words and get the actual clip back, not a list of near-misses.
How does automatic content tagging work - do we still need manual logging?
Vision and language models tag people, places, topics and moments as content comes in, so manual logging isn't needed for the baseline metadata - editors review and refine rather than starting from a blank log.
Can this help us measure ad revenue across broadcast, streaming and social consistently?
Yes - audience analytics unifies viewing behaviour across broadcast and digital, so content decisions and ad pricing are backed by one shared picture instead of separate, conflicting platform reports.
Does automated compliance review replace human broadcast standards review?
No - it pre-screens content against broadcast codes and flags language, claims and restricted material before human review, so your compliance team spends time on genuine judgment calls instead of first-pass scanning.
How do you measure whether the AI's tagging is actually accurate enough to trust?
We pilot on one programme archive or channel and measure tagging accuracy directly against your own editors' judgement before expanding, and producers and researchers review search quality with us before it rolls out further.
Can this handle old footage indexed only by filename or tape label?
Yes - that's exactly the archive discoverability problem this is built for. Old footage gets tagged and made searchable in plain language, regardless of how poorly it was originally logged.
Start the conversation
Have one archive
worth searching properly?
Tell us what's hardest to find in your library. Give us a sample of your archive and we'll come back with a working search demo and a realistic plan for indexing the rest.