AI-Driven R&D Process with Ultrasonic Wind Estimation Data
In R&D projects exploring new domains, reliable data and AI-driven processes help teams move from uncertainty to validated product decisions faster. Vuono Group partnered with EXC Group, a global product development and manufacturing company in outdoor sports, to accelerate the development of a new ultrasonic wind estimation technology.
“Vuono Group helped accelerate our R&D project in a completely new domain. Their proactive approach, deep technical expertise and high-quality deliverables helped us quickly understand what was possible and move forward with confidence.”
The Challenge
EXC Group had launched an R&D project to develop a new product capable of measuring wind speed and direction with multiple ultrasonic sensors. While the demo hardware could capture measurement data, the team lacked a validated software baseline and reliable datasets for evaluating algorithms. Key design decisions such as compute capacity, transmission sequence, and sampling strategy were difficult to justify without reliable data, and carried significant technical risk.
To accelerate product development, EXC Group needed deep data and AI expertise combined with hands-on software engineering and R&D experience. The goal was to enable systematic evaluation of algorithms using both measured and synthetic data and to provide clear recommendations on how to move forward.
The Solution
We established an AI-driven R&D workflow combining data-driven experimentation and systematic benchmarking to drive faster iteration in algorithm and system design. By building an end-to-end signal processing pipeline and introducing synthetic data for controlled experimentation, we created a framework for systematic evaluation of algorithms and reduced risks in new product development.
The pipeline extended from raw sensor data streams to wind estimation outputs through a configurable, modular software architecture optimized for embedded systems.
Using synthetic data generation and repeatable benchmarking workflows, we systematically tested different algorithms, parameters and hardware configurations against key evaluation criteria: accuracy, robustness, and runtime constraints.
AI-assisted software engineering accelerated the experimentation workflows and improved the reliability of analysis results throughout the project.
This structured process enabled the team to compare design options objectively and identify viable technical directions for the product.
Key Achievements
Vuono Group's collaboration with EXC Group established an AI-driven R&D process for ultrasonic wind estimation, providing a structured foundation for technical decision-making in a new product domain.
Systematic evaluation to reduce risk and accelerate iteration: A repeatable experimentation framework combining measured and synthetic data enabled objective comparison of algorithms, parameters and hardware configurations. This clarified key design decisions and reduced technical risks.
AI-driven R&D as a competitive advantage: Integrating AI-assisted tooling into the experimentation workflow accelerated iteration cycles, improved output quality, and maximized value generation throughout the product development process.
A modular software baseline ready for embedded development: The adaptable codebase provides a strong foundation for further hardware revisions and production-oriented embedded software optimisation.
About EXC Group
EXC Group is a global product development and manufacturing company with operations across three continents. The company’s core business focuses on developing, designing and manufacturing products for shooting sports, paintball and airsoft, and in addition to its own in-house brands, it serves customers worldwide through ODM and OEM solutions tailored to their needs. With an in-house manufacturing facility in Taichung, Taiwan, EXC supports rapid development and integrated production across plastics, metals and electronics.
Eero Siivola
This project was driven by Eero, Vuono Group’s Data Scientist (Ph.D in Machine Learning) with 10+ years of experience applying advanced analytics across domains to solve complex problems, redesign processes, and drive measurable business impact.