The first thing you notice is the border. A video uploads, and a few seconds later, the API has stripped away the black bars, the letterboxing, the empty space that cameras and cropping leave behind. It’s a small, surgical fix, one of dozens of discrete tasks in Sieve’s documentation. But it points to the larger, messier problem the company is now solving: before an AI can understand a video, someone has to clean it, index it, and make it searchable at a scale that defies human effort.
Founded in 2022 by Mokshith Voodarla and Abhinav Ayalur, Sieve began as a developer tool, a Video AI API that abstracted the infrastructure needed to run machine-learning models on video [Sieve, November 2022]. The pitch was practical. Instead of building custom pipelines for ingestion, processing, and search, a developer could call an endpoint. The company has since widened its aperture considerably. Today, Sieve calls itself a "multimodal data lab," providing the high-quality video, audio, image, and interaction data that frontier AI systems are trained on [Sieve]. Its infrastructure now processes hundreds of millions of media files daily for customers that include AI labs, Fortune 100 companies, and generative AI startups [Sieve].
From API endpoints to data environments
The pivot is a story of following the compute. As AI models grew hungrier for multimodal training data, especially video, the bottleneck shifted from raw processing power to curated, structured data. Sieve’s initial API business gave it a front-row seat to this demand. The company now describes its core offering as combining large-scale infrastructure, multimodal understanding, data sourcing, and research partnerships to build "the data and environments frontier AI labs use to train the next generation of multimodal systems" [Y Combinator]. This means moving beyond offering tools to developers and toward being a primary supplier of the feedstock for advanced AI in areas like generative media, robotics, and agentic systems.
A look at the company’s public job postings reveals the technical ambition underpinning this shift. Open roles are heavy on distributed systems and applied research, seeking engineers to build the exabyte-scale video infrastructure the company claims to operate [Built In San Francisco].
The founding wedge and investor conviction
The founders, Voodarla and Ayalur, started the company building developer tools for computer vision, a background that grounds their current work in practical engineering [Sieve, March 2026]. Voodarla’s prior experience at Scale AI and NVIDIA provided a direct view into the data challenges of training large models [Apple Podcasts, October 2024]. This founder-market fit convinced a tier-one group of early backers to write a seed check.
| Date | Round | Amount | Lead Investor |
|---|---|---|---|
| November 2022 | Seed | $4,000,000 | Matrix Partners [Sieve, November 2022] |
The $4 million seed round was led by Matrix Partners, with participation from Y Combinator (where Sieve was part of the Winter 2022 batch), Swift Ventures, AI Grant, and a roster of angel investors including former Scale AI executive Lucy Guo and roboticist Eric Jang [Sieve, November 2022]. While a recent Y Combinator job posting references a subsequent Series A from the same investor group, specific details on that round are not publicly verifiable [Y Combinator, January 2026].
An infrastructure built for precision
Sieve’s product surface is a catalog of specific capabilities that, together, form a data refinement pipeline. It’s not a single monolithic model, but a suite of tools for transforming unstructured media into training-ready datasets.
- Video understanding and search. The original Video AI API allows developers to programmatically process, understand, and search video content at scale, a foundational capability that remains core [Sieve, November 2022].
- Specialized media APIs. The company offers targeted APIs for tasks like text-to-video lipsync and the automated detection and removal of video borders, which are critical for creating clean, consistent training data [Sieve].
- Partnership-driven distribution. A collaboration with online video editor Kapwing to launch "AI Personas" shows Sieve embedding its technology into creator workflows, a potential channel for sourcing novel data [Sieve].
The business model is developer-friendly, with API pricing based on the parameters used while running a job, rather than flat subscriptions [Sieve].
The crowded field of vision
Sieve’s repositioning places it in a competitive arena with several distinct types of players. The risk is that as AI giants build their own data pipelines and open-source tooling improves, a middle-layer infrastructure provider could be squeezed.
- Specialized AI platforms. Competitors like Twelve Labs (video understanding) and Roboflow (computer vision datasets) attack specific slices of the multimodal data problem.
- Cloud hyperscalers. Services like Amazon Rekognition Video offer broad, general-purpose media analysis, competing on ecosystem and scale.
- Open-source frameworks. Tools like Voxel51’s FiftyOne provide a free, locally-hostable alternative for dataset management and visualization.
Sieve’s answer appears to be a focus on depth, quality, and vertical integration. By controlling the entire stack from raw data ingestion to curated dataset delivery, and by targeting the most demanding "frontier" AI labs, the company bets it can offer something more bespoke and higher-fidelity than a generic cloud API or a DIY open-source assembly.
What frontier AI labs are buying
The ultimate test for Sieve’s bet is what its customers are actually building. The company does not name them, but its claimed client categories,frontier labs, Fortune 100 firms, AI startups,suggest the data is fueling ambitious projects in robotics, world models, and generative media [Y Combinator]. This is the highest-stakes segment of the AI market, where data quality directly translates to model performance breakthroughs. For these buyers, the cost of poor or noisy training data is existential, which theoretically grants a provider like Sieve significant pricing power and retention, provided it can consistently deliver.
The cultural question Sieve is implicitly answering is one of trust in an age of synthetic media. As AI-generated video becomes commonplace, the value of authenticated, high-fidelity, real-world video data for training and evaluation only increases. Sieve is building the infrastructure for that verification, the pipeline that turns a chaotic universe of video into a structured, searchable, and ultimately understandable corpus. It’s a bet that the future of AI won’t just be about models, but about the quality of the world we feed them.
Sources
- [Sieve, November 2022] Sieve's Video AI API Beta and ~$4M Raise | https://www.sieve.ai/blog/launch
- [Sieve] Sieve, The multimodal data lab | https://www.sieve.ai/
- [Y Combinator] Sieve: The multimodal data lab | Y Combinator | https://www.ycombinator.com/companies/sieve
- [Sieve, March 2026] Reintroducing Sieve | https://www.sieve.ai/blog/reintro
- [Apple Podcasts, October 2024] #7 - Mokshith Voodarla, CEO Sieve - Inside The Workflow | https://podcasts.apple.com/us/podcast/7-mokshith-voodarla-ceo-sieve-building-the-future/id1751304457?i=1000669350950
- [Y Combinator, January 2026] Product Engineer at Sieve | https://www.ycombinator.com/companies/sieve/jobs/R7esADT-product-engineer
- [Built In San Francisco] Built In San Francisco profile | https://www.builtinsf.com/company/sieve
- [Software Engineering Daily] Video Search with Mokshith Voodarla - Software Engineering Daily | https://softwareengineeringdaily.com/podcasts/video-search-with-mokshith-voodarla/