What does success look like for Nyckel, and where do you see the company going in the next five years if everything goes well?

The good news and the bad news are that our use cases are extremely broad. I think every company in the world could potentially be a Nyckel customer because every company in the world has some text or image or some tabular data that they want to classify, search, or index. So we are doubling down on the platform play. 

Bigger companies already have their own ML team, and those companies are a little bit hesitant to throw it all away and use a Nyckel API instead. So most of our companies are earlier in their trajectory and our goal is to find more and more of those. Our goal is to become the household name for machine learning among non-experts, the same way people think of Stripe for payments or Twilio for telecom. 

The way we achieve that is to do two things. First, we double down on this simple abstraction where we hide all ML complexity behind a simple API at the data layer

Second, is to broaden our ML product offering. There are many different types of Machine Learning use cases. Do you want to classify something, search for something, detect things, or do named entity recognition? We try to build out these function types and have as much coverage as possible for anything people want to do that falls under the machine learning umbrella.