GM Financial Services @ String

Hey this is Rohit (CEO/co-founder) writing this. I find most JDs to be quite impersonal and bland. We do things a bit differently here at String. So to start off, I appreciate you for taking the time to read this and for considering us.

We’re building the best infrastructure for humans and agents to use the web. Until now, using the web meant clicking through a browser by hand. That’s quickly giving way to using agents that search for information, take actions, and build datasets. String’s universal API lets our customers do all of this across billions of pages, on a web that was never designed for machines.

Customers ranging from the world’s largest hedge funds to seed-stage startups use String to access hundreds of millions of web pages per week (that’s literally 100+ pages every second).

We’re generating 7 figures in ARR from financial services with no sales people. We have found initial PMF and are now hiring to accelerate growth. We’ve done everything so far with a lean engineering team and we recently raised a round from leading VCs and angels in our space (Auren Hoffman, Charlie Songhurst, and many more).

Half of our sales so far have come inbound and we haven’t churned a customer. We are known in financial services as the best product and therefore have many financial institutions reaching out to work with us.

We work with dozens of the top hedge funds, VCs, and banks in the world including 6 of the 10 largest hedge funds.

We are looking for the right person to lead our financial services business as we expand to more verticals! This will be a very entrepreneurial job where you’re responsible for growing the FS business while making the product more valuable for clients.

Today any HF/VC/Bank that wants to collect web data has to jump through many hoops:

Due to all these challenges, web data is very different from other straightforward datasets they ingest like credit card data. We solve all these problems for them so that they never have to worry about their web data pipelines.

As a result, we 1) significantly improve their data pipelines and therefore decisions driving millions in revenue and 2) save them millions of dollars in engineering costs of building it themselves.