Sieve

Sieve captures and aggregates data across real-world, digital, and simulated environments. That covers video, audio, images, and interaction traces, so a team doesn't have to stitch together several vendors for each data type. The company also builds custom collection around the specific capabilities a research team wants to improve, and it supplies audio-visual AI data alongside text-heavy sets.

Raw footage is noisy. Sieve scores incoming data for semantics, rights, artifacts, and task quality before anything ships. This filtering step matters because low-quality or rights-questionable clips can quietly poison a training run, and fixing that after the fact is far more costly than screening it up front.

Sieve indexes billions of videos, images, audio clips, and interaction traces using purpose-built detectors and embeddings. A tutorial on its blog describes embedding one billion videos and querying roughly 41 billion vectors. For buyers, that scale translates into searchable datasets rather than a folder dump.

Data arrives with captions, transcripts, object labels, action metadata, camera signals, UI events, and custom schemas. Human QA runs alongside the video labeling. The result is curated multimodal datasets instead of raw files a team must tag themselves.

Sieve supports filtering, licensing, consent, retention, and permission requirements tied to a client's training needs. Delivery runs with end-to-end encryption, custom retention, and SOC 2 Type 2 controls. For legal and procurement teams, that paperwork often decides whether a deal clears at all.

Organizations that own content libraries, speech recordings, or interactive worlds can license them through Sieve. The company scopes a pilot and agrees terms before any capture starts, which gives rights holders a structured route to paid AI data deals.