How did Nyckel achieve its initial product-market fit and what does its core customer profile look like in terms of company type and buyer role?

The core customer profile is basically either a CTO or a senior product manager at a small to medium-sized startup. 

One of our first big customers was a Brazilian fintech company, Foregon.hey had a problem with spam where people signed up, and they used profile pictures that weren't their own profile pictures. They were able to train a Nyckel function to say, “Is this profile picture actually a real person or one of the fake ones from the library of fake photos that they use?” The customer there was their tech lead. He just created an account, uploaded his own data, trained the model, and put it in deployment in a day or two without talking to us or asking us for help. He did it in a pretty self-serve manner.

I think the beauty of machine learning is that we're seeing very diverse use cases at Nyckel. We have a company called Gardyn that builds in-house intelligent gardens. They are equipped with cameras and stuff, and they take pictures of the plants, and they send them to Nyckel to check if it is wilting. Does it have fruit on it right now? Is it pests? That's on the computer vision side. 

We have a company called SpyScape that builds an augmented reality game where you walk around the world taking pictures with your phone. They also use Nyckel to add intelligence and a semantic layer to the game.

The last one I want to mention is a company called Taimi, which is a dating community of the LGBTQ. They use Nyckel for content moderation—Is this comment suggestive or not? Are they trying to scam you, or is it too explicit? In their case, because it is a dating community, they can't really use off-the-shelf content moderation models because the bar is just a little different, so they needed a custom solution, and we were able to help them with that.