Sieve is a data cleaning platform based in New York City, NY, USA[2] that combines artificial intelligence with human review[1]. The company was part of Y Combinator's Spring 2025 batch[3]. Sieve addresses data cleaning challenges for hedge funds and investment firms by enabling them to obtain clean data through a simple four-line code implementation[4]. The platform provides an API that integrates into existing data pipelines, allowing users to send data for processing instead of manually routing it to engineers for review[4]. Sieve employs AI agents designed specifically for financial data collection alongside expert-in-the-loop review to deliver validated data[4]. The service is accessible via API or Excel[1].
About
- sieve solves data cleaning for hedge funds and investment firms by letting them get clean data in four lines of code. Currently, their data pipelines have conditions that raise for human review, which literally send an email to engineers with data that needs to be reviewed. We provide an API that integrates directly into their existing pipeline - instead of raising for human review, they can send all the same information to our API and get clean, high-quality data back. By using our AI agents built specifically for financial data collection, along with expert-in-the-loop review, we provide our clients with clean, validated data at a scale and level of quality that wasn't achievable before.[4]
- sieve solves data cleaning for hedge funds and investment firms by letting them get clean data in four lines of code. Currently, their data pipelines have conditions that raise for human review, which literally send an email to engineers with data that needs to be reviewed. We provide an API that integrates directly into their existing pipeline - instead of raising for human review, they can send all the same information to our API and get clean, high-quality data back. By using our AI agents built specifically for financial data collection, along with expert-in-the-loop review, we provide our clients with clean, validated data at a scale and level of quality that wasn't achievable before.[9]
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