Our Investment in MX Labs
Big vision, successful repeat founders and technology that can literally save lives. There’s a lot to like in MX Labs, our most recent investment at SMOK.vc. Let’s break it down!
The Vision
Most people around the world don’t have access to up-to-the-minute and readily-available diagnostics. Instruments are expensive and the healthcare systems are overloaded.
Yet, almost everyone has cellphones. MX Labs turns mobile phones into mini diagnostic systems to prevent health problems from happening or stop before they get serious.
I find this vision very appealing. Access to health care used to be a genuine problem globally even before COVID-19. Now it affects not only the developing nations but nearly everyone regardless of where they live.
Being able to self-diagnose at home daily and spot problems while scrolling your favorite social media app sounds like a dream, but as the technology advances, the dream is finally becoming possible.
Tech like what MX Labs is building paired with a robust tele-medicine solution to connect patients with real doctors when a problem is spotted feels like the future of healthcare.
We’re excited to potentially be a small part of it!
The Founders
Like with every startup we invest in, also here, it all started with the founders. Both Remi Kościelny and Przemek Jaworski are serial entrepreneurs with years of experience building and scaling tech companies.
Remi co-founded one of the most successful mobile game studios in CEE region, Vivid Games, (where our partner Paul Bragiel was an early investor) which he took to the Warsaw Stock Exchange.
He recently left the company after recording its biggest year to date in terms of revenue.
Przemek has been involved in a number of startups, including ZMorph, a 3D printing hardware company where he raised ~$3M in VC funding.
Remi is a hustler and a frontend guy, Przemek is a hacker and a backend ninja. They do seem like a dream pair of founders, don’t they?
The Technology
Video cameras built into modern smartphones are powerful and very sensitive light sensors that can capture subtle changes in human skin color and texture.
They can also register and track tiny physiological details that are normally invisible to the naked eye.
Coupled with powerful processors and smart algorithms, able to perform millions of calculations per second, they can tell more about your health than many dedicated medical devices.
Inspired by the possibility of capturing such wealth of data from the skin, the MX Labs team committed themselves to creating a simple, yet powerful video-based diagnostic platform: Shen.AI. How does it work?
- Once the facial video image is detected and stabilized, it is passed into two simultaneous computation streams.
- One extracts the signal from blood pulsations in the skin vasculature, and is able to detect heart rate, blood oxygen saturation and blood pressure.
- The other, in the meantime, is able to examine skin texture and build its color map model used for facial skin health diagnostics.
- Together they form a unique data processing pipeline, which ultimately connects to an AI-driven diagnostic engine.
The Co-Investors
Once we’re committed to investing, we love to bring to the table others, who we feel can add a lot of value to the founding team.
In case of MX Labs we were able to complete this pre-seed round with four magnificent investors from all around the globe:
- Joseph Mocanu via his Singapore-based Verge HealthTech Fund who brings 20 years of experience in developing and investing in healthcare technologies.
- Aaron Patzer — a New Zealand based entrepreneur known for co-founding and selling Mint.com, who has recently been involved in digital health projects like Carbon Health, Radix Health or Vital Software
- Ragnar Sass and Martin Tajur — Estonian angel investors, co-founders of Pipedrive, a unicorn from the Baltics
Conclusion
We’re very excited to work with such a group of entrepreneurs and investors and I’m sure they will all add value once the company grows.
You can read the rest of “Why we invested in” collection here:
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