Alumni profile: Quix solution to online applications

Tell us about Quix

Quix is a platform that provides an end-to-end development environment for building data-driven applications. It allows developers to build applications using live data streams and real-time machine learning – develop, test and deploy all in one place.

We built Quix around an in-memory data store called a message broker, in contrast to existing data and machine learning platforms that are architected around disk-based data storage. Quix provides our customers with lower latency, higher bandwidth and greater efficiency when compared to processing data in the traditional manner. Quix is particularly suitable for any applications where time is a driving context such as smart factory, connected mobility, robotics, healthcare and financial services. We also provide a free tier to help developers start building without any costs.

I'm passionate about applying technology to solve real-world problems and have enjoyed every moment of bringing Quix to market. My door is always open to anyone who wants to discuss their ideas - just reach out, I’d love to pay forward some of the support I have received!

Application users benefit from a more intuitive customer experience, providing genuine value for both the user and the business.

Where did the idea for Quix come from?

I met my co-founders, Tomáš Neubauer, Péter Nagy and Patrick Mira Pedrol, at McLaren where we developed solutions to get more data from the track to the factory in real-time. With each car producing 1.1 million data points per second it quickly became apparent that our databases were a significant bottleneck because each data point has to be written-to and read-from a disk before it can be processed. Our final solution was to adopt an open source message broker called ‘Apache kafka’ which keeps data in-memory, significantly reducing the latency and cost of working with this volume of live data. The biggest problem we found is that compared to databases, message brokers are very difficult to work with, especially for our race engineers and data scientists who increasingly use Python to write code. We saw it as an industry wide problem and founded Quix to make it easier for Python developers to use message broker technologies in their applications.

What lessons have you learned throughout the startup process so far?

Raising our Seed funding was much harder than we imagined! A working product and early adoption in the market is a must, but we were surprised about how much detail early stage investors wanted on the go-to-market strategy, including proof that certain marketing and sales strategies actually work.

My advice to other entrepreneurs would be to focus on commercialising your MVP, set up coherent KPI's as soon as possible and let the numbers speak for themselves. I'd also say that most investors aren't going to be the right partner for you, so work hard to filter them early to avoid time consuming dead end conversations.