WATCH WEBINAR ON-DEMAND
Kaizen Copilot Demo: Process Analysis in Minutes, Not Days
Traditional time and motion studies, line balancing, and process documentation are manual, time-consuming, and hard to keep current. Kaizen Copilot turns a simple smartphone video of a work process into structured analysis in minutes, not days or weeks.
In this product webinar, we’ll demonstrate 5 of the most popular modules of Kaizen Copilot, an AI-powered software that turns a simple smartphone video of a process into structured analysis in minutes, not days or weeks.
- Station Design: Automatically break down an assembly process into meaningful steps, measure time for each step, and provide instant recommendations to improve layout and workflow efficiency.
- Line Balancing: Calculate optimal task distribution, visualize workload balance, and simulate different scenarios in a fraction of the time.
- Digital Work Instructions: Generate clear, step-by-step instructions directly from a task video.
- Ergonomics: Run analyses such as REBA, RULA, NIOSH lifting equation, Snook tables, and Hand Strain Index from recorded work.
- Floor Analysis: Map operator movement, identify inefficiencies, and recommend optimized layouts to reduce walking time without sacrificing safety or productivity.
If you work in continuous improvement, industrial engineering, or operations, this is a practical look at what AI can do on the shop floor today.
Presenters:
Jesse Aust, VP of Global Enterprise Sales, Retrocausal
Jesse Aust is the Vice President of Global Enterpise Sales at Retrocausal, where he spearheads efforts to deliver cutting-edge AI and computer vision solutions that empower frontline workers and optimize manufacturing processes. With over 15 years of experience in enterprise software sales and five years in strategic advisory roles, Jesse has a proven track record of helping manufacturers solve mission-critical challenges.
At Retrocausal, Jesse’s mission is to enhance efficiency, improve training, and elevate performance on the shop floor by leveraging intelligence augmentation systems. His deep expertise and customer-focused approach enable manufacturers to achieve their business goals and drive transformative outcomes.
Hamza Khan, Lead Machine Learning Engineer, Retrocausal
Hamza Khan leads the Machine Learning team at Retrocausal, where he develops ergonomics-focused AI systems for manufacturing and industrial environments. His work centers on computer vision and machine learning methods for 3D ergonomic assessment, motion analysis, and automated time-and-motion studies using video data. By applying human-centered AI to workplace analysis, he focuses on improving safety, efficiency, and comfort across industrial operations.