The lack of deep, holistic data analytics from wearables limits the applications and personalization of health recommendations for the multi-billion dollar consumer health device industry. Watz, a consumer biotechnology AI startup founded by Alessandro Latif, “ingests thousands of performance data points and biomarkers to deliver insights to athletes, coaches, and consumers that maximize their performance.” The Miami, Florida-based startup has raised $2 million round from Sophon Ventures.
Watz currently has eight employees. The startup was founded in 2020. Watz plans to spend its newly raised funds on research & development, operations, office space and branding. Watz is competing with a number of companies including Apple, Fitbit, and other wearable device makers in both the hardware and software domains.
Frederick Daso: What health insights do current fitness wearables fail to communicate to their wearers, and why?
Alessandro Latif: Current fitness wearables use standardized metrics and assumptions to drive many of their scores. Since a wearable can generally only track one or two components of health, the metrics they provide are based on guesses as to how an average person would perform in the health components it is unable to track. For example, a wearable that gives you a strain score, or a recovery score, is typically based on flawed models that attempt to generalize the entire population and ignore many other components of strain and recovery (like nutrition, stress, physiology, and more) . We’ve found that these strain scores are frequently inaccurate because they attempt to simplify complex problems.
Since current fitness wearables cannot convey the whole health picture, users must guess which fragments need improvement. For “Biohackers” (anyone attempting to optimize their health), their approach relies on A/B testing products, training plans, and other health components to work out which combinations may be producing a personally beneficial response. This is usually a stab in the dark. They either have to rely on instinct (what felt better?) or on limited wearables (did my sleep score improve last night?). Since they cannot observe all of the health components together, they are unable to isolate each of the dependent variables. In other words, they cannot accurately identify which pieces of the health puzzle have contributed to their improved sleep. It could be that their room was cooler or darker, or they didn’t stay up late watching TV. Without total picture analysis, this is guess work!
Daso: Among elite athletes and fitness enthusiasts, what is the potential impact for them to receive more granular, detailed data about how their body performs?
Latif: One of the biggest benefits Watz provides is hyper-personalizing insight. No two humans respond the same way to training. At the same time, every coach has a different opinion on how to train, recover, or eat. This forces individual athletes to comply with a coaching strategy that may not optimally suit their bodies.
Data and metrics without insight cannot fully support decision-making. Current solutions typically normalize a population rather than adjust to individual differences. As a result, they struggle to predict exactly how someone will respond to training tweaks.
Watz ensures they make gold-medal-winning training decisions.
Because Watz models an athlete’s entire physiology, we can more accurately predict the way their body responds to inputs. This is hyper-personalization at a level previously unheard of.
Daso: What will you discover from high-performance fitness professionals that will allow you to serve consumer and enterprise customers in the long term?
Latif: Unlocking the secrets of human performance at the biological level.
The marginal gains philosophy of elite sports requires you to go into microscopic detail to produce performance. This detail has allowed us to build more accurate models, which is not possible using the starting point of everyday consumers (who don’t follow training plans, forget to start their wearable, or skip a meal).
However, the insights we have uncovered are not just for elite athletes. Whether you have a desk job or run professionally, our biological systems are the same. The performance output changes, but the inputs remain unchanged (mindset, sleep, strain, nutrition). Regardless of output, we can optimize these inputs and ensure peak performance in any domain.
Daso: How will you cultivate community through Watz? How does that strategy lend itself to a more robust machine learning output for Watz users?
Latif: Currently, our focus is on creating a community of elite sports scientists at the forefront of physiology, biomechanics, psychology and nutrition. The fire that brings this community together is the desire to learn more about what makes a human perform at their best. This hunger for insight is what brings our community together.
Cross-collaboration among this group is hugely important to us. Due to the human body’s interconnectedness, new insights in one field help drive discoveries in another. We ensure this cross-collaboration takes place both on and off the platform through forums and in-person events that we host.
Because of this hunger to learn new insights, the collaboration we foster, and the ability to develop new unique insights with the help of machine learning, our community is incentivized to keep using Watz. Watz allows them to stay at the forefront of their respective fields, developing the greatest possible intelligence on optimal human performance.
Daso: What additions to the Watz’ analytical layer’ will be built in the future?
Watz: Our community is constantly adding to the analytical layer of Watz. It is a place for anyone who understands how to interpret biometric data to build new metrics and insights. The creators of these insights can then share them with a community of top athletes, coaches and teams. This feature will soon be publicly available so that anyone can contribute to building the health algorithms of our future.
The analytical layer can host an ecosystem of applications, such as remotely monitoring patients or helping insurance companies better quantify the risk of their policies. The platform improves the insight we stand to gain from biomarkers; the final piece of the puzzle, though, is directing the output of this analysis. This is the grand plan, but it requires careful steps to ensure we succeed in decoding the human health puzzle.
Daso: What’s one emerging trait that is collectively shared across your team that will enable Watz’s mission to be realized?
Latif: I think one of the strongest unifying threads is that we are/were all high-performance athletes. We know the level of detail involved in winning championships, and we know that to get there requires a meticulous learning process.
People often talk about creating learning cultures, but it takes a lot of patience and focus on being reflective when we fail. This is tricky because we often internalize failure as something that hurts us, but by structuring in this deliberate reflection, we learn the opportunity that comes with failure—the opportunity to learn something new. By learning as a team, we learn from each other’s mistakes and not just our own, so our learning rate is a lot higher together.
We also came from sports, where data is a central part of this learning process. As a racing driver, I would spend hours debriefing with race engineers after every session, often spending 8 hours a day analyzing every detail of data, informing where the car could improve. We are deeply acute to the edge we could gain by learning more from data.