sieve GTM brain

Sieve extracts and validates data from documents and websites for hedge funds and other data-intensive teams using AI and expert review.

category

Financial data extraction and validation

market profile

  • text: Investment firms assemble proprietary datasets from fragmented, often unstructured sources and need confidence in the resulting figures.
  • text: Managed alternative-data extraction competes with both internal scraping teams and specialist providers.

personas

  • title: Head of alternative data at a hedge fund
    pains:
    • Building investable historical series from fragmented, inconsistent publications
    • Defending accuracy and provenance when research decisions depend on a dataset
  • title: Data engineering lead at an investment firm
    pains:
    • Maintaining brittle extraction pipelines across web pages and PDFs
    • Interrupting engineering work to resolve data quality exceptions
  • title: Investment research analyst
    pains:
    • Manually transferring financial figures from source documents into spreadsheets
    • Waiting for cleaned time-series data before testing an investment thesis

icp

  • text: Hedge funds and asset managers with substantial assets and dedicated alternative-data or quantitative research teams; Sieve cites clients with over $100B in assets under management.
  • text: Data-intensive technology companies operating hundreds of provider integrations are a secondary fit where reliable human review is valuable.

message pillars

  • text: Convert messy web and document sources into ready-to-use structured data.
  • text: Combine AI throughput and expert checking for higher-stakes extraction.
  • text: Use consensus review to reduce one-off human mistakes.

objections

  • objection: Can I trust AI-generated figures in investment research?
    response: Evaluate a sample against known source documents and agree accuracy and provenance checks; Sieve describes expert and consensus review.
  • objection: Will a human review step be too slow for our pipeline?
    response: Agree a turnaround target for a representative sample; Sieve acknowledges review adds latency and positions it for mission-critical work.
  • objection: Our sources are obscure and inconsistent.
    response: Provide representative documents or pages for the overnight sample the company offers.

competitors

  • name: Forage AI
    why: Managed and custom web extraction for alternative-data buyers overlaps Sieve's custom sourcing.
  • name: YipitData
    why: Alternative-data vendor competing for hedge fund data budgets and research workflows.
  • name: Canoe Intelligence
    why: Extracts data from alternative-investment documents, overlapping the document-processing use case.

keywords

  • hedge fund alternative data extraction
  • financial document data extraction
  • human verified AI data extraction
  • investment research web scraping
  • financial data cleaning API
  • historical investment data extraction

voice

  • practical and accuracy-focused
  • technical but accessible
  • direct about review latency

signals

  • text: A fund launches a research initiative requiring historical datasets from fragmented port records or niche publications.
  • text: A data team is scaling provider integrations and needs a repeatable review process for exceptions.

stack

  • Excel
  • API

pricing

model: unknown

motion

hybrid

channels

  • SEO
  • case studies
  • free web scraping agent
  • launch coverage

confidence

medium

Founders

  • name: Nicole Lu
    title: Founder/CEO
    role: CEO
    bio: Nicole is co-founder and CEO of sieve, which is solving data cleaning for hedge funds and investment firms. She previously worked at Citadel across Equity Quant Research, Global Trading, and Data Science groups, was a management consultant at McKinsey, and developed cancer detection algorithms at the Broad Institute.

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