Type a sentence, find the shot in seconds — across millions of archive hours
Manually scrubbing through master tapes can eat half a day. Kaster combines VLM semantic search with Whisper auto-captioning to pinpoint the right frame even in footage that was never tagged — cutting search time from hours to seconds.
* Supports on-prem-first deployment — raw footage never uploaded, never migrated
| Query | Results | Confidence |
|---|---|---|
| "outdoor rain shots" | 14 clips | 92% |
| "mayor interview clips" | 6 clips | 88% |
| "nighttime traffic accident" | 3 clips | 71% |
Find any moment in your archive with a single sentence
Semantic video search
Editors used to scrub through footage frame by frame, hunting from memory — a half-day job at best. Now a VLM (vision-language model) understands every segment of the footage and builds a semantic index. Even if the material was never manually tagged and metadata is completely blank, typing "find every outdoor rain shot" pulls up the matching clips instantly — turning hours of tape-hunting into a few seconds of searching.
Whisper AI auto-captions and transcripts
No more transcribing footage line by line by hand. Speech is auto-transcribed into searchable transcripts and SRT caption files, so the captions and transcripts platforms require are ready the moment you need them — speeding up delivery.
On-prem-first indexing
No need to migrate your entire archive, re-encode files, or run security sign-off just to move to the cloud. The Edge Agent connects directly to your existing NAS structure — your archive stays exactly where it is, and the original video never leaves your facility. Only semantic derivatives go to the cloud, and they're never used to train models for other customers or third parties, so your security and IT teams don't have to lose sleep over it.
Face / scene-assisted recognition
A five-dimension scoring system automatically screens footage and flags duplicate or near-duplicate clips, cutting master tape inventory and rights-clearance work from days down to hours.
Who's burning hours just trying to find a shot
News archive retrieval
When producing a feature or retrospective, you no longer need to rely on memory to figure out which tape had a certain interviewee or scene — one sentence pulls the matching clip from years of archive in seconds.
Rights clearance & duplicate detection
A five-dimension scoring system automatically flags duplicate, near-duplicate, or potentially infringing clips, turning rights clearance from clip-by-clip manual review into automated batch screening.
Evidence lookup & internal knowledge bases
When enterprises or government agencies need to pull footage of a specific event from large volumes of recordings, natural-language search replaces manual frame-by-frame review, speeding up retrieval and delivery.
What you might still want to know about AI video search
Q1Can footage be found even without pre-existing tags or metadata?
Yes. The VLM semantic index is built by understanding the content of each segment directly — it doesn't depend on manually pre-tagged metadata. Even completely untagged footage can be found using a natural-language description of what's in the frame.
Q2Does the raw video need to be uploaded to the cloud to build the semantic index?
No. The Edge Agent analyzes footage on-site by connecting directly to your existing NAS structure, uploading only audio summaries, keyframes, and semantic embeddings — roughly 1-5% of the original file size — to the cloud. The raw video stays in your facility the entire time.
Q3Are the derivatives sent to the cloud ever used to train models for other customers or third parties?
No. Derivatives are used solely to build a dedicated search index for your archive — they are never retained to train models unrelated to your organization, and temporary cloud data is automatically deleted under a lifecycle policy.
Q4How is the search confidence score calculated, and what if it's low?
The confidence score reflects how closely the VLM judges the visual content to match the semantics of your query. A low score usually means the query description is vague or the visual features are ambiguous — try adding more specific details (scene, subject, or action) to improve match accuracy.
Q5What languages does Whisper auto-transcription support, and how accurate is it?
It supports multilingual speech recognition, though real-world accuracy varies with recording quality, accent, and background noise. We recommend validating transcription and search accuracy on your own archive footage through a 100-hour POC before committing to a full rollout.