Our product
We provide clean, human-validated data in four lines of code
Make data quality a "set and forget" process with our API
requests.post(
"https://api.usesieve.com/api/v1/process",
json={
"request_type": "quarter_end_date_batch",
"metadata": { "companies": [s for s in symbols],
"callback_url": "https://etl.bouldercap.com/api/callback/"}})How it works
Replace all your manual data cleaning and extraction workflows with a simple call to our API
Our AI + human-in-the-loop approach ensures you get the highest quality data, scalably and reliably
We build on top of the latest frontier models and use our proprietary in-house data to fine-tune the models for improved performance
AI-led extraction
Based on your request, we use AI to track down the relevant source documents and extract the data you need
Human expert review
After AI extraction, the data is reviewed by a team of expert human reviewers to ensure the data was extracted cleanly and accurately
Consensus validation
Once all experts have reviewed the data, we use consensus validation to ensure the data is accurate. This means we are robust to one-off errors - no more fat finger errors!
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
"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."
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."
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."
Senior Data Scientist
Leading ESG investor
"The problem sieve is solving is something all big companies deal with. The state of the art at my company is to send emails to our own employees when data fails QA in production"
Data Pipeline Engineer
Leading data engineering consulting firm
"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
