Turn PDFs, reports, contracts, and filings into structured data
AI-powered extraction with human-level accuracy. Validated by experts, delivered ready to use.
Extracted Data
Company Name
Acme Corporation
Total Revenue
$394.3B
Revenue Chart
Quarterly Revenue Data:
Net Income
$96.99B
Fiscal Quarter
Q4 2023
How it works
sieve adapts where traditional parsers break
Traditional document processing fails when PDFs have inconsistent layouts, tables spanning pages, or scanned images. Our AI adapts to any format while human experts catch edge cases.
Edge cases we handle:
- • Inconsistent layouts: Different table structures across pages or documents
- • Scanned documents: Poor quality images, handwritten notes, stamps overlapping text
- • Complex tables: Merged cells, nested headers, tables spanning multiple pages
- • Multi-format files: Mixed content types (text, images, tables) in a single document
- • Contextual data: Understanding footnotes, cross-references, and implicit relationships
AI-led extraction
Our AI understands document structure contextually, not just by position. It adapts to inconsistent formats and extracts data even from scanned images.
No brittle XPath selectors or hardcoded coordinates that break when layouts change.
Human expert review
Expert reviewers catch edge cases like ambiguous table boundaries, overlapping stamps on scanned docs, or footnotes that modify primary data.
Eliminates the "95% accurate, but needs user review for the last 5%" problem.
Consensus validation
Multiple reviewers validate each extraction. If there's disagreement on a complex case, we escalate for resolution - no silent failures.
Robust to OCR misreads, ambiguous formatting, and one-off errors.
Our clients
Trusted by leading hedge funds
Powering data-driven decisions for the world's most sophisticated investment firms
$100B+
in Assets Under Management
100K+
Data Points Extracted & Validated
Our investors
Backed by the best
backed by world-class investors who believe in our vision
Questions you may be wondering
Case studies
Real-life case studies
See how sieve has helped businesses improve their data operations
Feedback
What People Say
"As a former CEO of a global asset manager, I know firsthand how much time and money is wasted when investment teams have to clean bad data or build in-house fixes that never scale. Poor data quality is one of the most persistent - and costly - headaches in our industry. sieve has solved this problem at the root.
By combining AI with rigorous human validation, they've created the clean data layer that our industry has needed for decades. Spotless data isn't a nice-to-have, it's a top priority, and sieve finally delivers it - freeing teams to focus on strategy and performance rather than cleaning data."
Aaron Meder
Former CEO, L&G – Asset Management, America
"The quality of even the most standard datasets is appallingly poor. It's a complete waste of my QRs' time to track this stuff down. It's absolutely ridiculous that we do this. It's a waste of time and money, especially when every hedge fund in the world is doing it"
"The quality of even the most standard datasets is appallingly poor. It's a complete waste of my QRs' time to track this stuff down. It's absolutely ridiculous that we do this. It's a waste of time and money, especially when every hedge fund in the world is doing it"
Portfolio Manager
Leading quantitative hedge fund
"Data quality is a huge issue. I've been thinking about this problem for years but haven't found a solution."
"Data quality is a huge issue. I've been thinking about this problem for years but haven't found a solution."
Trader
Family office
"This is exactly what I've been looking for. Data quality is a huge issue. We can't rely on any single vendor."
"This is exactly what I've been looking for. Data quality is a huge issue. We can't rely on any single vendor."
Data Engineer
Top 5 investment bank
"I tried to fix our data quality in-house. There were a bunch of steps to make it all work and the biggest issue is I still had to check all the data manually. It took weeks of time to check it all."
"I tried to fix our data quality in-house. There were a bunch of steps to make it all work and the biggest issue is I still had to check all the data manually. It took weeks of time to check it all."
Senior Data Scientist
Leading ESG investor
"This is a pain-point most people have but don't want to deal with. These problems cost a lot of money and we'd like to make them just go away"
"This is a pain-point most people have but don't want to deal with. These problems cost a lot of money and we'd like to make them just go away"
Manager of Enterprise Data
Leading international bank
