Ilya Valmianski and Alejandro Zaniolo are Building Material Model to Automate Industrial Video Analysis with AI
The manufacturing sector is the backbone of the items we use every day, from the cars we drive to the clothes we wear. Industrial engineers are the architects of this sector, designing processes to create high-quality products at scale.
But the factory floor is complex, and industrial engineers often spend many hours per week painstakingly annotating video footage to understand what’s going on in the production line.
Having spent years working on AI solutions for healthcare, Ilya Valmianski recognized the opportunity to use the power of LLMs to solve the complexity of the physical world. So he set out to build Material Model to automate industrial video analysis for manufacturing and build the data foundation for a robotics-led manufacturing future.
Introducing Material Model: AI-Powered Video Analysis for Manufacturing
Material Model uses AI to analyze manufacturing process videos so that industrial engineers can reduce manual annotation and free time for high-level strategic work.
Many industrial engineers spend as much as 14 hours per week annotating and studying manufacturing process videos. Workers on the ground may deviate from standard operating procedures, or take action based on tribal knowledge that isn’t shared beyond the factory floor. Industrial engineers need to understand what’s going on in order to optimize manufacturing processes, but “annotating factory footage frame-by-frame can take 3-4x video length,” explains Ilya, adding up to thousands of hours per year for each engineer.
Material Model enables engineers to automate video annotation and identify process deviations instantaneously, freeing them to spend more time on high-level work like process optimization and factory layout planning. The platform offers features such as MODAPTS support (a motion classification system used by industrial engineers), step-level tracking, ergonomics indicators, evidence clips, and export formats that allow engineers to customize analysis and tailor processes to their unique workflows.
Having only launched six months ago, Material Model is already working with major manufacturers across various sectors, including automotive.
Ilya’s journey to Material Model
Ilya’s road to Material Model has been far from straightforward. He began his career obtaining a PhD in Physics and completing research in the field. But before long he felt an urge to work on things that would impact people directly, and pivoted to work in healthcare with a focus on ML applications.
Spending time at organizations such as Kaiser Permanente and Curai Health, Ilya developed innovative ML applications that helped healthcare organizations increase efficiency and improve patient care.
Ilya’s experience in healthcare soon led him to launch his own company, MDandMe, the first truly conversational medical AI. MDandMe raised notable funding and helped over 100k patients, including those with many co-morbidities and complex symptomatology, find the care they need, while assisting their physicians in providing the best possible service.
But after recognizing the rate at which AI was advancing, Ilya pivoted to focus on AI’s interactions in the physical world, particularly with a focus on “disembodied embodiment”—the concept of AI engaging with the physical world without traditional robotics. Although there was lots of focus on AI as the “doctor of the future,” he believed that there was an underserved opportunity to bring the power of AI to the physical spaces of manufacturing.
After meeting his co-founder, Alejandro Zaniolo, through Y Combinator’s founder network, Ilya and Alejandro will undergo YC’s Winter 2026 cohort as they continue to develop the platform and partner with leading manufacturing companies across the country.
Building for the future of manufacturing
As the manufacturing sector transitions from human-led to robotics-led, the team aim for Material Model to serve as the training infrastructure that enables companies to design effective robotics processes.
Many manufacturing steps require highly dextrous tasks which robots aren’t yet able to perform. By performing granular analysis on current human-led manufacturing processes, Material Model will enable companies to train robots for complex tasks as robotics technology continues to advance.
And as companies make the transition, the platform will help companies understand robotic system failures and human-robot interactions so that they can advance their processes without compromising production.
To learn more about Ilya and Alejandro’s journey, you can follow Ilya on LinkedIn, follow Alejandro on LinkedIn, and visit materialmodel.com/
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