Sieve
The multimodal data lab providing high-quality video, audio, image, and interaction data for frontier AI.
Website: https://sieve.ai/
Cover Block
Open sources
| Attribute | Details |
|---|---|
| Company Name | Sieve |
| Tagline | The multimodal data lab providing high-quality video, audio, image, and interaction data for frontier AI. [Sieve] |
| Headquarters | San Francisco, US |
| Founded | 2022 |
| Stage | Seed |
| Business Model | API / Developer Platform |
| Industry | Deeptech |
| Technology | AI / Machine Learning |
| Geography | North America |
| Growth Profile | Venture Scale |
| Founding Team | Co-Founders (2) |
| Funding Label | Seed (total disclosed ~$4,000,000) |
Links
Open sources
- Website: https://www.sieve.ai/
- LinkedIn: https://www.linkedin.com/company/sievedata
- GitHub: https://github.com/sievedata
What an Investor Needs First
Open sources
Sieve is building the data infrastructure for the next generation of multimodal AI, a bet that positions the company at the center of a critical and expanding bottleneck for frontier labs and large enterprises. Founded in 2022 by Mokshith Voodarla and Abhinav Ayalur, the company began as a developer platform for video AI but has since broadened its scope to become a "multimodal data lab" providing curated video, audio, image, and interaction data [Sieve, March 2026]. The founders' initial experience building developer tools for computer vision has evolved into a focus on delivering high-quality, structured data at scale, processing hundreds of millions of media files daily to serve customers ranging from Fortune 100 companies to generative AI startups [Sieve, November 2022].
Voodarla's prior roles at NVIDIA and Scale AI bring relevant exposure to both the hardware and data annotation layers of the AI stack, while the CTO's background remains focused on the core technical challenge [Async 2025, 2024]. The company secured approximately $4 million in seed funding in November 2022, led by Matrix Partners with participation from Y Combinator and Swift Ventures, and a subsequent Series A is referenced but not yet detailed in public filings [Sieve, November 2022]. Its business model is API-driven, with pricing based on computational parameters, targeting developers and research teams directly. Over the next 12-18 months, the key indicators to monitor will be the verification of the Series A round details, the disclosure of specific anchor customers from its claimed Fortune 100 and frontier lab base, and the technical differentiation of its data curation pipeline against a growing field of competitors.
Verified against public records -- Core company facts confirmed by Y Combinator, company blog, and investor announcements.
Taxonomy Snapshot
| Axis | Classification |
|---|---|
| Stage | Seed |
| Business Model | API / Developer Platform |
| Industry / Vertical | Deeptech |
| Technology Type | AI / Machine Learning |
| Geography | North America |
| Growth Profile | Venture Scale |
| Founding Team | Co-Founders (2) |
| Funding | ~$4,000,000 (Seed) |
Inside the Company
Open sources
Sieve was founded in 2022 by Mokshith Voodarla and Abhinav Ayalur, who began the company building developer tools for computer vision [Sieve, March 2026]. The company is headquartered in San Francisco and operates as a venture-scale seed-stage business [Y Combinator]. Its initial public milestone arrived in November 2022 with the launch of a Video AI API beta and the announcement of an approximately $4 million seed round led by Matrix Partners [Sieve, November 2022].
A subsequent repositioning in early 2026 marked a shift in scope. The company's March 2026 announcement, titled "Reintroducing Sieve," framed the business as a "multimodal data lab" focused on providing high-quality video, audio, image, and interaction data for frontier AI labs, Fortune 100 companies, and generative AI startups [Sieve, March 2026]. This move expanded the company's stated mission beyond its original API wedge toward a broader infrastructure and data supply role.
Verified against public records -- Confirmed by company announcements and Y Combinator profile.
Under the Hood
Reported and inferred
Sieve's product evolution is a case study in market adaptation, moving from a focused developer tool to a broader infrastructure platform. The company launched in 2022 with a Video AI API, a cloud-native platform designed to abstract the infrastructure required to process, understand, and search video at scale for machine learning workflows [Sieve, November 2022]. This initial wedge targeted developers and ML teams who otherwise would have to assemble their own video ingestion and processing pipelines. The core value proposition was operational simplicity, offering APIs for tasks like border detection and removal, automated video search, and a Text To Video Lipsync API [Sieve].
Today, the positioning has expanded significantly to what the company calls "the multimodal data lab." The product suite now aims to provide the high-quality video, audio, image, and interaction data required to train frontier AI systems [Sieve]. This includes not just processing infrastructure but also curated datasets, such as coherent video scenes and before-and-after media pairs for editing tasks. The company claims to process hundreds of millions of media files daily, operating at exabyte scale [Sieve] [Built In San Francisco]. The buyer has consequently broadened from individual developer teams to include frontier AI labs, Fortune 100 companies, and generative AI startups working on robotics, computer use, and agentic systems [Y Combinator].
- Core Infrastructure. The backend is a distributed system for multimodal data, inferred from job postings for Systems Engineer and Distributed Systems Engineer roles that require expertise in scaling data-intensive applications [Y Combinator, January 2026].
- Pricing Model. APIs are priced based on the parameters used while running a job, suggesting a consumption-based model aligned with developer platform norms [Sieve].
- Partnership Surface. A publicly announced partnership with Kapwing to launch AI Personas demonstrates an early go-to-market channel into creative tools [Sieve].
Partially corroborated -- Product claims are sourced from the company's own materials and Y Combinator profile; technical stack details are inferred from job postings.
Market Research
Open sources
The market for high-quality, multimodal training data is not a niche but a foundational constraint for the next wave of AI systems, which increasingly require understanding and interacting with the physical world through video, audio, and sensor inputs.
A precise total addressable market (TAM) figure for multimodal AI data infrastructure is not publicly available from a named third-party report for Sieve. However, the demand context is anchored by adjacent, well-documented markets. The global market for AI training data, broadly, was valued at $2.5 billion in 2022 and is projected to reach $8.2 billion by 2028, according to a report from MarketsandMarkets [MarketsandMarkets, 2023]. More specifically, the market for video analytics software, which relies on similar underlying data processing, was estimated at $8.2 billion in 2023 and is forecast to grow to $33.5 billion by 2032 [Precedence Research, 2024]. These analogous markets illustrate the scale of the opportunity Sieve is addressing, even as its specific wedge focuses on the more complex, frontier-grade data required for generative media, robotics, and agentic systems.
Demand is driven by several converging tailwinds. The primary driver is the shift from large language models (LLMs) to large multimodal models (LMMs) that can process video, audio, and images. This shift, led by labs like OpenAI (GPT-4V), Google (Gemini), and Anthropic (Claude 3), creates an acute need for vast, curated, and synchronized multimodal datasets [Sieve, March 2026]. A secondary driver is the proliferation of generative AI applications in media and entertainment, which require specific data pairs for controlled editing and generation tasks. Finally, the push toward embodied AI and robotics necessitates interaction data that captures physical cause and effect, a category Sieve explicitly targets.
Key adjacent and substitute markets include general-purpose cloud AI services (e.g., Amazon Rekognition, Google Video AI) and open-source multimodal models (e.g., Qwen-VL). These markets represent both potential partners and competitive pressure. The primary substitute, however, remains in-house data operations. Large AI labs and Fortune 100 companies have historically built custom pipelines for data collection and labeling, a costly and engineering-intensive process that Sieve aims to abstract away [Software Engineering Daily].
Regulatory and macro forces are nascent but material. Data privacy regulations (GDPR, CCPA) and evolving copyright frameworks around AI training data impose compliance overhead on data sourcing and curation. A macro force is the increasing scarcity and cost of high-quality, rights-cleared data, which elevates the value of proprietary datasets and efficient data infrastructure.
AI Training Data (Broad) 2022 | 2.5 | $B
AI Training Data (Broad) 2028 | 8.2 | $B
Video Analytics Software 2023 | 8.2 | $B
Video Analytics Software 2032 | 33.5 | $B
The projected growth in these adjacent sectors, from billions to tens of billions, underscores the substantial economic activity flowing toward AI data and video intelligence. While Sieve's specific SAM is narrower, operating at the frontier of multimodal data, the underlying market currents are strong and well-funded.
Partially corroborated -- Market sizing figures are from third-party reports for analogous sectors, not Sieve's specific niche. Demand drivers are inferred from company positioning and industry trends.
Competition and Substitutes
Reported and inferred Sieve's competitive position is defined by its pivot from a developer API to a supplier of curated multimodal data, a move that shifts its primary battlefield from infrastructure tooling to data quality and research partnerships.
| Company | Positioning | Stage / Funding | Notable Differentiator | Source |
|---|---|---|---|---|
| Sieve | Multimodal data lab providing high-quality video, audio, image, and interaction data for frontier AI training. | Seed ($4M, 2022) / Series A (unverified) | Focus on curated, high-quality datasets and environments for frontier AI labs; exabyte-scale video infrastructure. | [Sieve, March 2026] |
| Twelve Labs | Developer platform for multimodal video understanding and search APIs. | Series A ($50M, 2023) | Core technology centered on proprietary video foundation models for deep semantic search. | [Crunchbase, 2023] |
| Roboflow | End-to-end platform for computer vision (data management, labeling, training, deployment). | Series B ($50M, 2024) | Comprehensive workflow tooling for CV teams, strong open-source community. | [Crunchbase, 2024] |
| Scale AI | Provider of high-quality training data and evaluation for AI applications. | Series F ($1B+ total) | Massive scale, long-standing enterprise contracts, and a broad data labeling workforce. | [Crunchbase, 2024] |
| V7 Go | Automated data annotation platform for image and video. | Acquired (2024) | Specialization in automated annotation powered by generative AI models. | [TechCrunch, 2024] |
The competitive map splits into three primary segments. First, in video understanding APIs, Sieve's original wedge, direct challengers like Twelve Labs offer similar developer-facing APIs but often emphasize proprietary models for search and classification [Crunchbase, 2023]. Second, in the broader data platform category, companies like Roboflow and Scale AI compete for the budgets of teams building and training models, though their focus is more on the annotation and pipeline management layer rather than Sieve's emphasis on pre-curated, high-fidelity datasets [Crunchbase, 2024]. Third, a set of adjacent substitutes includes large cloud providers like Amazon Rekognition Video, which offer generalized AI services, and open-source multimodal models like Qwen3-VL, which reduce the need for external data processing APIs altogether.
Sieve's current defensible edge appears to be its claimed focus on data quality and research alignment. The company's messaging targets "frontier AI labs" specifically, suggesting a product tuned for the exacting requirements of training cutting-edge systems, a niche less served by generalist platforms [Sieve, March 2026]. This edge is potentially durable if Sieve can maintain exclusive or privileged access to unique data sources or develop superior curation methodologies. However, it is also perishable; data quality is a difficult moat to defend as competitors can replicate sourcing strategies or simply purchase similar data.
The company's most significant exposure lies in its narrow commercial footprint and unverified scale transition. While it lists Fortune 100 companies as customers, no specific names are disclosed, making it difficult to assess enterprise traction against a competitor like Scale AI, which publicly serves major automotive and technology firms [Crunchbase, 2024]. Furthermore, Sieve's repositioning as a data lab places it in a capital-intensive space where success often correlates with fundraising capacity. Its last verified round was a $4 million seed in 2022; a subsequent Series A is cited in job postings but not independently verified, creating a potential capital gap against well-funded rivals [Y Combinator, January 2026].
A plausible 18-month scenario hinges on the frontier AI market's trajectory. If demand for ultra-high-quality, multimodal training data accelerates sharply, Sieve could emerge as a winner by solidifying partnerships with a few key labs, becoming a de facto supplier. Conversely, if large AI labs continue to build internal data pipelines or if generalist platforms like Scale AI successfully launch competing curated data offerings, Sieve could be a loser, squeezed between vertically integrated customers and better-capitalized, broader competitors. The outcome likely depends on Sieve's ability to convert its early technical focus into a commercial footprint that matches its ambitious positioning.
Partially corroborated -- Competitor profiles and funding stages are drawn from public databases, but Sieve's Series A status and specific competitive advantages rely on company statements.
Opportunity
Open sources The prize for Sieve is to become the foundational data layer for the next generation of multimodal AI, a role that could command a premium as critical infrastructure in a market projected to grow from billions to tens of billions in the coming decade.
The headline opportunity is for Sieve to define the category of frontier AI data infrastructure, becoming the default supplier of high-quality, curated multimodal datasets for labs training advanced systems in robotics, world models, and generative media. This outcome is reachable because the company has already established a technical wedge processing hundreds of millions of media files daily and is explicitly targeting the exact customer segments,frontier labs, Fortune 100 companies, and AI startups,that are scaling these efforts [Sieve]; [Y Combinator]. The shift from a general-purpose video API to a specialized data lab, articulated in the March 2026 reintroduction, signals a strategic focus on the most demanding and valuable part of the AI stack: the training data itself [Sieve, March 2026].
Multiple paths could drive significant scale. The following scenarios outline concrete trajectories supported by the company's current positioning and partnerships.
| Scenario | What happens | Catalyst | Why it's plausible |
|---|---|---|---|
| Dominant Supplier to Frontier Labs | Sieve becomes the primary external data source for 3-5 leading AI labs, embedding its datasets into major model training runs. | A public research partnership or co-authored paper with a named lab validates the quality and impact of Sieve's data. | The company's tagline and customer description are already explicitly oriented toward "frontier AI labs" [Sieve]; [Y Combinator]. Its infrastructure handles exabyte-scale video, a prerequisite for such deals [Built In San Francisco]. |
| Enterprise Video Intelligence Standard | Fortune 100 companies standardize on Sieve's platform for internal video search, analytics, and content generation, driving high-ACV enterprise contracts. | A flagship deployment with a named Fortune 100 customer in media, retail, or security is announced. | Sieve lists Fortune 100 companies as a core customer segment and offers an automated video search platform described as fitting internal tools [Software Engineering Daily]; [Y Combinator]. |
| API-Driven Ecosystem Lock-in | Sieve's APIs become the embedded multimodal data engine for a wave of generative AI startups, creating a distribution network. | A partnership similar to the Kapwing AI Personas launch is replicated with several other high-growth application layer companies [Sieve]. | The company originated as a developer platform, and its pricing is based on API usage parameters, indicating a product built for integration [Sieve]. |
Compounding for Sieve would manifest as a data flywheel. Each major customer engagement generates more proprietary, high-fidelity data pairs and interaction logs. This growing corpus could improve the quality of future datasets, create more valuable "before-and-after" editing pairs for controlled generation, and allow Sieve to benchmark and curate data against real-world model performance. Early evidence of this dynamic includes the claim of processing "hundreds of millions of media files per day," suggesting operational scale that feeds back into dataset refinement [Sieve]. A partnership like the one with Kapwing to launch AI Personas demonstrates an initial step toward embedding its technology into other products, which could generate unique interaction data [Sieve].
The size of the win can be framed by looking at comparable infrastructure providers in adjacent data-centric markets. Companies like Scale AI (private, last valued at over $7 billion) and Roboflow (a competitor in the computer vision data space) illustrate the valuation potential for platforms that become essential to AI development pipelines. If the "Dominant Supplier to Frontier Labs" scenario plays out, Sieve's position would be analogous to a specialized, high-margin data vendor within a massive total addressable market. While no specific TAM figure is publicly cited for multimodal training data, the scale of investment in foundation models suggests the infrastructure supporting them could support a multi-billion dollar standalone business. A successful execution on its stated mission could place the company in a position for a significant strategic acquisition or an IPO at a premium to standard SaaS multiples, given the scarcity of pure-play, scaled providers in this niche.
Partially corroborated -- The core opportunity narrative is derived from the company's own stated positioning and target customer segments, which are consistently cited. The plausibility of growth scenarios is supported by these statements and early partnership evidence, but specific catalysts and comparable outcomes remain forward-looking.
Sources
Open sources
[Sieve] Sieve , The multimodal data lab | https://www.sieve.ai/
[Sieve, March 2026] Reintroducing Sieve | https://www.sieve.ai/blog/reintro
[Y Combinator] Sieve: The multimodal data lab | https://www.ycombinator.com/companies/sieve
[Sieve, November 2022] Sieve's Video AI API Beta and ~$4M Raise | https://www.sieve.ai/blog/launch
[Async 2025, 2024] Home - Async 2025 | https://async2025.com/
[Built In San Francisco] Built In San Francisco | https://www.builtin.com/san-francisco
[Y Combinator, January 2026] Product Engineer at Sieve | https://www.ycombinator.com/companies/sieve/jobs/R7esADT-product-engineer
[Software Engineering Daily] Video Search with Mokshith Voodarla - Software Engineering Daily | https://softwareengineeringdaily.com/podcasts/video-search-with-mokshith-voodarla/
[MarketsandMarkets, 2023] AI Training Data Market | https://www.marketsandmarkets.com/Market-Reports/ai-training-dataset-market-183812351.html
[Precedence Research, 2024] Video Analytics Market | https://www.precedenceresearch.com/video-analytics-market
[Crunchbase, 2023] Twelve Labs - Crunchbase Company Profile | https://www.crunchbase.com/organization/twelve-labs
[Crunchbase, 2024] Roboflow - Crunchbase Company Profile | https://www.crunchbase.com/organization/roboflow
[Crunchbase, 2024] Scale AI - Crunchbase Company Profile | https://www.crunchbase.com/organization/scale-ai
[TechCrunch, 2024] V7 Go Acquired | https://techcrunch.com/2024/01/16/v7-go-acquired/
Articles about Sieve
- Sieve's Video AI API Processes Hundreds of Millions of Media Files a Day — The YC-backed startup, now a 'multimodal data lab,' supplies curated video and audio data to frontier AI labs and Fortune 100 companies.