Turn websites into structured, reliable data at scale
AI-powered extraction with human-level accuracy. Validated by experts, delivered ready to use.
Extracted Data
Capture URL
example-store.com/products
Extract Name
Premium Widget Pro
Extract Price
$299
Inspect Network
🌐 From API (not on page):
How it works
sieve adapts where traditional scrapers break
Traditional scrapers fail when sites change their HTML structure, use dynamic JavaScript, or implement anti-bot measures. Our AI understands content semantically and adapts to changes automatically.
Edge cases we handle:
- • Dynamic JavaScript content: SPAs, infinite scroll, lazy-loaded data, and client-side rendering
- • Changing HTML structures: Sites that constantly update their class names or DOM layout
- • Anti-scraping measures: CAPTCHAs, rate limiting, IP blocks, and fingerprinting detection
- • Complex interactions: Multi-step forms, login walls, cookie consent, and AJAX pagination
- • Inconsistent data formats: Mixed data presentation across pages or sections
AI-led extraction
Our AI understands content semantically, not by CSS selectors. When sites change their HTML, our extraction continues working without manual updates.
No brittle XPath or CSS selectors that break with every site redesign.
Human expert review
Expert reviewers confirm the AI extracted all data exactly correctly and that no edge cases cause inaccurate or failed extraction to slip through the cracks.
Eliminates the "mostly works but silently fails on edge cases" problem.
Consensus validation
Multiple reviewers verify each extraction and we look for consensus before marking data as correct. This ensures we're resilient to human mistakes, not just AI errors.
Robust to human error.
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
