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.
New content arrives constantly, and each piece needs metadata, captions, and a version for every platform. Manual teams fall behind fast.
Old footage indexed by filename and tape label. Producers know the shot exists somewhere. Finding it takes far longer than it should.
Ad spend spread across broadcast, streaming, and social with no shared way to measure it. Which platform actually earned the revenue stays a guess.
Every piece gets cut, reformatted, and re-captioned for each platform by hand. The same show ends up shipped several different ways.
Vision and language models that tag people, places, topics, and moments automatically as content comes in, no manual logging needed.
Describe the shot you need in ordinary words. Search across transcripts, tags, and visual features returns the clip, not a list of near-misses.
Viewing behaviour unified across broadcast and digital, so content decisions and ad pricing are backed by one clear picture, not two conflicting ones.
Models that pre-screen content against broadcast codes, flagging language, claims, and restricted material before it reaches human review.
We map your ingest, MAM, and distribution workflows, and check archive quality before proposing any pipeline.
We start with one programme archive or one channel and measure tagging accuracy against your own editors' judgement.
Producers and researchers review search quality with us. Once it holds up, indexing expands collection by collection.
Models get tuned as house style and platforms change. Your media ops team runs the pipeline day to day.
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.
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.
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.
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.
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.
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
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.
