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About
Sieve builds multimodal data and environments for frontier AI labs developing systems for generative media, robotics, computer use, world models, and agentic applications. It sells to frontier AI labs, Fortune 100 companies, and fast-growing AI startups, differentiating through exabyte-scale infrastructure, multimodal sourcing and understanding, specialized indexing and annotation, and research partnerships.
Market
Sieve operates in AI-native web and document data extraction and data-quality infrastructure for financial research and investment data pipelines. It positions itself as a high-accuracy alternative to manual cleaning, in-house LLM workflows, traditional data vendors, and broader finance research platforms by combining AI-based source discovery and extraction with expert review and consensus validation. Its differentiation is a human-validated, API-delivered data layer that can monitor fragmented or off-vendor sources, including small-cap company filings, while early YC-reported results indicated 60–70% lower cost than existing options.
Ideal customers are sophisticated hedge funds and other investment firms, especially teams managing fragmented, mission-critical financial, ESG, commodities, and public-company data or seeking coverage beyond traditional vendors. Primary users and buyers are quant researchers, data scientists, data engineers, and investment research teams that need clean, validated data delivered into existing workflows through an API or Excel.
At a Glance
Problem
Investment firms routinely need reliable data from fragmented, unstructured sources such as websites, SEC filings, PDFs, obscure reports, and other publications. The work is both operationally painful and economically costly: existing pipelines often flag records for engineers or analysts to review manually, while poor data quality wastes researchers’ time and can require teams to build in-house fixes that do not scale. Even seemingly basic financial facts, such as earnings dates, can be difficult to collect accurately.
Sieve’s central use case is replacing this manual review layer in hedge-fund data pipelines. Its early results claim a 100% match with internally hand-collected data, turning weeks of full-time review into a passive background process and reducing costs by 60–70% versus existing options, including BPO outsourcing.
Product / Service
Sieve is an AI-powered data extraction and quality platform delivered primarily through an API, with access also available through tools such as Excel. Its AI agents locate and retrieve the relevant source documents, extract the requested information, and then route each data point to expert reviewers. The reviewed results are returned as clean, validated data that can flow directly into an investment firm’s existing systems, reportedly in as little as four lines of code.
The product combines the scalability and speed of automated extraction with the accuracy of human-in-the-loop validation. This is especially valuable for mission-critical financial workflows where an AI-only system may produce unacceptable errors, while the customer’s own engineers and analysts can focus on differentiated research rather than repetitive data cleaning.
Market
Sieve competes in investment data engineering, alternative-data extraction, and AI-assisted data-quality software for hedge funds and other investment firms. Its competitive set includes manual internal review, custom in-house data pipelines, and BPO outsourcing, as well as adjacent financial-data platforms such as Daloopa. Sieve’s claimed differentiation is handling bespoke web and document sources while combining AI extraction with expert validation, rather than only supplying standardized datasets.
The company appears to be an early but commercially oriented startup rather than a clearly pre-revenue research project. Y Combinator lists it as an active Spring 2025 company founded in 2025, and Sieve says it is backed by leading investors, serves hedge funds, and has produced case studies covering trade records, historical publications, commodities data, bitcoin-mining metrics, and small-cap financials. The public evidence supports early customer validation and active go-to-market activity, but does not disclose revenue, customer counts, or contract values.
Founders & Leadership
Funding History
Clocktower Technology Ventures
Y Combinator
Recent News
A Startup Intros profile describes Sieve as an AI-powered data-cleaning and validation platform for hedge funds and investment firms. It reports that the company has raised $4.5 million across two rounds, most recently a $500,000 seed round in June 2025.
Sieve documentation introduced its Excel add-in for extracting financial data from PDFs and SEC filings directly within Excel, extending the company’s financial-data workflow into spreadsheets.
Active Roles
8Business Model
Sieve sells access to ready-to-use and custom training datasets, evaluation sets, and AI environments. Purchases are structured through agreements based on data volume, task complexity, and annotation requirements, with custom deliveries available under service-level agreements.