Skip to content

WEBINAR ON-DEMAND

Know Before You Automate: Introducing Assembly Automation Assessment from Kaizen Copilot

Deciding whether an assembly process is worth automating is one of the most expensive judgment calls on the floor.
 
Get it wrong and you've sunk budget into a process that was never a good fit — or passed on an easy win. Yet most teams still make that call from experience and gut feel, or wait weeks on a manual feasibility study.

Our new Automation solution within Kaizen Copilot gives you a faster, defensible answer. It reads your process video and engineering drawings and produces a transparent Automation Complexity Score, with a full breakdown of the factors making a process easy or hard to automate, so you can see the "why" behind the number before you invest.
 
In this webinar, we'll walk-through the workflow and show you how to:

  • Turn a process video into steps: Mark one assembly cycle and let the system break it into elemental operations, each with a description and value type you can review and edit.
  • Read your drawings automatically: Extract assembly and part factors (such as joining methods, geometry, symmetry, materials, tolerances, and handling requirements) straight from your assembly and part drawings.
  • Classify each step: Decide whether an operation can be automated with a known method or requires novel engineering, and see how that feeds the score.
  • Get a transparent complexity score: Find out whether a process is easy, moderately complex, or highly complex to automate, with a factor-by-factor breakdown.
  • Act on the results: Pinpoint which parts of the process to redesign before committing to automation.

 

Webinar Speaker:

Aprameya Manjunath-edit

Aprameya Manjunath, Director of Solutions Architecture, Retrocausal

Aprameya Manjunath is the Director of Solutions Architecture at Retrocausal, where he leads the development of Kaizen Copilot, an AI-powered Industrial engineering analysis tool. Over the past few years, he has worked with top U.S. manufacturers to transform manual Industrial engineering assessments into automated, AI-driven solutions. Earlier in his career, he served as a manufacturing engineer at a leading automotive company, collaborating with shopfloor teams to improve efficiency and quality. With a unique blend of expertise in industrial engineering and computer vision technology, Aprameya brings both practitioner-level insight and product innovation experience, bridging the gap between traditional IE practices and next-generation AI solutions.