Pydantic, the popular Python library and open source data-validation framework used by some of the world's biggest companies, has a new commercial namesake and the backing of one of Silicon Valley's most storied venture capital (VC) firms.

Pydantic Services Inc. emerges from stealth today with $4.7 million in seed funding led by Sequoia, with participation from ParTech, Irregular Expressions and a host of angel investors, including Zapier co-founder Bryan Helmig, dbt Labs founder Tristan Handy and Sentry co-founder David Cramer.

London-based software developer Samuel Colvin started Pydantic as an experiment back in 2017, and in the intervening years the project has gone from strength to strength, used by developers at major tech companies including Alphabet, Amazon, Apple, Meta and Microsoft.

Its adoption has been driven in large part by FastAPI, a web framework for building APIs that integrates with Pydantic under the hood. However, Pydantic's growth more broadly can also be attributed to the explosion of Python, which overtook Java in 2019 to become the second most popular programming language after JavaScript.

According to Colvin, Pydantic now garners some 48 million downloads each month and is used by 19 of the top 25 Nasdaq-listed companies.

"Right now, 12% of professional web developers use Pydantic across a wide range of applications," Colvin explained to TechCrunch. "The pace at which developers have come to use and trust the tool showed me the scale of the problem and appetite for a better solution."

So what, exactly, do developers use Pydantic for?

Well, there are myriad scenarios where online applications need to check and validate the type of data a user enters. For example, a simple online form that requests a name, email address and phone number will need to reduce the chances of someone entering the wrong kind of data, so it can be helpful if the form can automatically check the email is in a valid format, or the name-field is not left blank. Similarly, a bank might build a new system for processing transfers that collects data from multiple internal and external sources -- this system has to ensure that the data is in the right format before any money transfer is executed.