Alex Ovcharov is the founder and CEO of Wayvee Analytics, a real-time customer satisfaction and engagement monitoring solution for retail, and the co-founder of Sensemitter. He has extensive experience in research, product development, and customer behavior analysis, gained through his roles as Product Director at Shazam Eastern Europe and through his entrepreneurial ventures.
His professional journey includes pioneering successful augmented reality (AR) campaigns at Shazam, and co-founding Sensemitter, a gaming experience analytics company. Inspired by a discovery in WiFi sensing, Alex and his team of developers and former CERN physicists introduced AI algorithms for emotional analysis, leading to Wayvee Analytics’s founding in May 2023. This innovation in Emotion AI is set to transform how retailers gain actionable insights on customer satisfaction and engagement, delivered in real-time without the use of cameras or surveys, all while respecting users’ privacy.
What inspired the founding of Wayvee Analytics, and how did your background with Shazam and Sensemitter contribute to this journey?
My experiences have shaped what we do at Wayvee, focusing on emotion recognition. During my time at Shazam Eastern Europe, I launched the region’s first Augmented Reality (AR) campaign and saw how facial expressions revealed emotional patterns. Leading a research project using facial coding, I realized many industries like retail were interested in such technology, though privacy and tech limitations were major challenges.
Combining my background in neuroscience and product development, I saw the need for better customer understanding in offline environments, where existing tools were either slow in feedback collection or privacy invasive. This led us, along with ex-CERN physicists, to develop Wayvee’s Emotion AI, overcoming these challenges with a technology that operates with radio waves, ensuring 100% customer privacy and delivering insights in real time.
Could you share more about the discovery in WiFi sensing that sparked the creation of Wayvee?
In May 2023, I came across an article that really piqued my interest – it was about Wi-Fi sensing for tracking human movement. It described how Wi-Fi-based devices could capture data on how people move, and how radio waves are extremely sensitive to these position changes. That got me thinking — if radio waves can detect movement, why couldn’t they also capture heart rate and breathing? These are key indicators for understanding emotional states.
Together with Viacheslav Matiunin, Wayvee’s CTO and a physicist who led data analysis for the LHCb experiment at CERN, and a group of researchers and neuroscientists, we built a prototype using a regular Wi-Fi router to test the idea. The team engineered an algorithm that could detect breathing and micro-movements using just Wi-Fi signals, and we patented the technology. This marked the beginning of developing our MVP and eventually our own hardware device – Wayvee sensor.
Wayvee was launched out of stealth in 2024. Can you talk about the initial goals of the company and how you envision transforming the retail analytics landscape?
As a deep tech company focused on Emotion AI for the physical world, we see various potential applications for this technology, from healthcare to smart homes. However, my experience in customer-facing markets quickly showed that retail had the greatest potential for impact. Retailers are constantly seeking ways to increase customer satisfaction and how to better understand their audience, yet they often rely on outdated methods that don’t provide real-time insights or face privacy concerns with personal data collection.
Through our pilot stage, it’s become clear that retailers need actionable insights, not just data. It’s not enough to simply identify unhappy customers — we help explain why and offer recommendations for immediate improvement, keeping customers satisfied in the moment.
Wayvee uses a privacy-preserving sensor with no cameras. How does your technology manage to capture physiological signals like breathing and heart rate using radio frequency (RF) waves?
For us, privacy is a big deal, and that’s why we don’t rely on cameras. Сameras obviously can track where someone is and what they’re doing, but interpreting emotions can be tricky, especially if the person’s position or angle throws it off. Can you imagine how many cameras you need to install to be able to see a person from different angles?
Instead, we use radio waves. The Wayvee sensor, installed on shelves or other key locations, emits radio signals and captures them when they bounce back, carrying a range of data — from breathing and heart rate to subtle shifts like posture, walking speed, and gestures. Our AI algorithms then process this data and convert it into emotional insights, recognizing if a person is angry, happy, neutral, etc.
Can you explain how the AI algorithm processes these physiological signals and translates them into actionable insights for retailers?
Wayvee devices capture radio wave signals, allowing our algorithms to identify objects and locate people. Our AI then analyzes their responses using a trained neural network based on the arousal-valence model, which assesses emotional intensity and positivity.
We focus on real-time emotional shifts rather than overall states, leveraging our extensive dataset to establish baselines for identifying emotions like happiness, sadness or frustration. This data is sent to a server that powers Wayvee, providing retailers with real-time analytics, including Customer Satisfaction (C-SAT), engagement metrics, and other insights. Retailers can generate custom reports and receive alerts for customer dissatisfaction, enabling immediate action.
What makes your approach to emotion AI, which is based on physiological signals like HRV and body gestures, more effective than traditional methods like surveys or video surveillance?
We bring everything together in one solution! Traditional surveys are slow, only capturing feedback from about 0.1% of customers, often resulting in biased responses. Our approach focuses on subconscious reactions, which are more accurate because they are involuntary. This allows us to cover 100% of customers interacting with a shelf and deliver real-time insights within about two minutes through our dashboard.
When it comes to video-based methods, they rely on cameras, which naturally raise privacy concerns, even when measures like face blurring are applied. We wanted to create a privacy-first solution that doesn’t make people feel like they’re being watched, which is why we’ve taken a different approach entirely — one that’s respectful of customer privacy while still delivering the insights retailers need.
How does Wayvee’s RF technology ensure customer privacy while still providing deep emotional insights?
It’s pretty simple — we don’t see people’s faces or identify their figures in a space. All the data we receive is fully anonymized. Unlike other solutions that blur faces or create 3D models to deal with privacy issues, we don’t have to do any of that because the way we gather information is totally different. We’re not working with visuals; it’s all done through signals, so privacy concerns just don’t come into play the same way.
Wayvee offers instant feedback on metrics like customer satisfaction (C-SAT) and engagement. How do these insights impact a retailer’s ability to make swift and effective operational changes?
At our core, we focus on delivering actionable insights for improvement. We go beyond metrics like dwell time and average speed, which can be relative but don’t tell the full story. The real value lies in combining these metrics with deeper insights that explain the results. With our data, retailers can optimize store layouts through A/B testing, experimenting with shelf arrangements, displays, and retail media to enhance customer satisfaction.
We also assist with workload planning by recommending resource allocation based on customer flow and engagement. For example, during a pilot project with a sneaker store, we discovered that faster customer movement correlated with higher purchases. Staff involvement was actually slowing down the process, so we suggested reducing staff during peak times, which increased sales. It’s amazing how small changes can have such a significant impact!
As more retailers adopt privacy-driven solutions, where do you see the future of in-store analytics heading? How do you plan to expand Wayvee’s technology and reach in the coming years?
I think the future of in-store analytics will definitely lean toward being more customer-focused. It’s not just about making their shopping experience smoother and more enjoyable, but also about respecting their privacy. With Wayvee, we have big plans ahead. Beyond what we’re already doing, there are so many potential use cases for our tech — whether it’s measuring the effectiveness of retail media or understanding how different types of content impact customers. We’re even looking into things like price prediction based on purchase intent. There’s so much opportunity to help retailers evolve while keeping their customers at the center of the shopping experience.
In terms of scalability, how easy is it for retailers to integrate Wayvee’s solution into their existing store infrastructure?
Our device is easy to install and requires minimal technical know-how, needing no ongoing maintenance. Retailers can set it up in just 10 to 30 minutes by attaching it to a shelf and setting the monitoring zone. Unlike camera systems, there’s no need for a large upfront installation. Retailers can start with a few sensors during a test period and expand as needed. Each device covers a 3.5-meter range, and once they send us their store layout, we’ll upload it to the dashboard for accurate data collection. All device data is centralized in one dashboard for easy monitoring and comparison.
Thank you for the great interview, readers who wish to learn more should visit Wayvee Analytics.
