Sieve builds data infrastructure for AI, with a focus on video. The company combines exabyte-scale video infrastructure with video understanding techniques to develop high-quality datasets for video modeling. Its platform handles sourcing, filtering, indexing, annotating, and delivering video, audio, image, and interaction data at scale, indexing billions of media items with purpose-built detectors and embeddings.
The team of 15 operates from San Francisco and is backed by Series A funding. Engineering work spans video data infrastructure, multimodal data processing, data indexing and annotation, and AI dataset development. Engineers have direct ownership over projects end-to-end, with an emphasis on writing clean, maintainable code while moving quickly.
Sieve's datasets are developed by scoring data for semantics, rights, artifacts, and task quality, then adding dense labels, pairings, temporal alignment, and human quality assurance at scale. The company's work sits at the intersection of AI research, data infrastructure, and multimodal AI.
