Problem

Frontier AI development is constrained by access to high-quality multimodal data: models increasingly need to understand how the world looks, sounds, moves, and changes over time, while data vendors often struggle to provide quality, scale, speed, and diversity simultaneously. At Sieve’s operating scale—hundreds of petabytes of video, equivalent to roughly 75 million hours of 1080p content per 100 petabytes—manual review is infeasible, and reactive sourcing causes unpredictable quality, delayed timelines, and inconsistent scale. The central economic pain is therefore not simply finding more media, but converting enormous raw inventories into precise, legally usable, consistently labeled training data without making collection, QA, and annotation a labor bottleneck.

The killer use case is supplying research teams with narrowly targeted, training-ready datasets for multimodal model development. Sieve focuses on applications including generative media and editing, visual understanding, robotics, world models, and computer use, where the value of a dataset depends on its distribution, temporal detail, labels, licensing, and ability to improve model outcomes—not merely on its size.

Product / Service

Sieve is a research-driven multimodal data lab and data-infrastructure provider. It captures and aggregates data from real-world, digital, and simulated environments; scores it for semantics, rights, artifacts, and task quality; indexes billions of videos, images, audio clips, and interaction traces; and adds dense labels, temporal alignment, transcripts, action metadata, pairings, and human QA. It then delivers secure, training-ready datasets, evaluation sets, and environments, with support for filtering, licensing, consent, retention, and permission requirements.

The delivery model combines ready-to-use datasets with custom engagements. Customers can request samples, define volume, distributions, metadata, licensing, QA, and format with Sieve, and purchase access based on data volume, task complexity, and annotations; pre-packaged datasets can arrive within days while custom data and environments are delivered on an SLA. Sieve’s software-first pipeline and human review are intended to improve yield and consistency while reducing rework, giving AI teams faster access to diverse, precisely matched data at scale, with secure transfer, encryption, custom retention, and SOC 2 Type 2 controls.

Market

Sieve competes in AI infrastructure and training-data services, specifically the emerging market for large-scale multimodal datasets, evaluation data, and environments for frontier models. It is differentiated from a conventional annotation vendor by coupling proactive sourcing with search, filtering, indexing, QA, and annotation: Sieve says it collects millions of hours of new content each month and has delivered hundreds of petabytes of video. Its competitive set includes broad AI-data providers such as Scale AI and Appen, human-intelligence and training-data companies such as Surge AI, and adjacent video or multimodal data platforms; the older Sieve product also overlapped with video-understanding API providers for translation, editing, and search.

Sieve is not pre-revenue based on the available evidence: by early 2025 it had real customers—including top creative tools, social platforms, and media companies—using its APIs in production, and by March 2026 it said it worked with many leading AI labs, Fortune 100 companies, and fast-growing AI startups. The company reports millions of dollars paid to content partners and hundreds of petabytes delivered, and its website says it is trusted by leading AI labs, Fortune 100 companies, and fast-growing AI startups. Its last clearly documented financing in the evidence is a roughly $4 million seed round announced in November 2022, led by Matrix Partners with participation from Y Combinator, Swift Ventures, AI Grant, and angels; no revenue figure is disclosed here.