What make Uncountable a fast growing company?
Uncountable is similar to other Silicon Valley companies in that we are a high-growth organization. We know that the value we're delivering today is not going to be the value we deliver in two or three years. We want to be helping our customers a lot more than we do today. That means growing a much bigger team, finding awesome people to work with, and really setting our sights high in terms of what we can achieve. We definitely think about what our product is going to be like in five years and how it's going to be a lot better than it is today.
Uncountable has always been bootstrapped. How does that change the way you think about company strategy and priorities compared to companies with VC funding?
The key priority for us is making sure that we're always driving towards adding more customer value. At the end of the day, raising money is something that can help but isn't going to make or break a company. It's really about whether or not a company can deliver and bring a product to the market that will make users happy. Being bootstrapped, we have the luxury of allowing ourselves longer time scales so we can think in terms of five years down the road and what this product will look like and build in that direction, rather than being constrained to a two-year funding cycle.
When you imagine the company in five years time, what are you the most excited about?
We are most excited about the type of team that we have the potential to build here. We've already brought in a lot of really awesome people, and it is what makes coming to work everyday a lot of fun. Hopefully in five years time, we have an even bigger organization with a more diverse set of people and skills in the building. That's what makes us most excited about how we're growing and what the future has store for us.
How is ML and AI in Uncountable different from what is already out there?
There is a lot of buzz right now about artificial intelligence and also a lot of uncertainty about what it really is and what it really means for the future of industries. As an ML engineer, you realize that ML is just a set of tools. It's a set of powerful tools, but at the end of the day it's more about how you apply them, what you’re building, and how they can really be used to drive change. The key aspect about bringing ML to a new industry is making sure that you can communicate its power effectively and honestly and then figuring out "here's the right application" and "here's what ML really means in this context.” That's both a challenge and something that I think we've done a good job of focusing on over the past couple years. There are a lot of applications out there that we can apply these tools to and we look forward to finding new ones and continue to work on the ones we have to make sure that they're applied in the right way.