I have a built-in marketing hype detector. I hear so much hype that sometimes it goes into overdrive. The hype cycle of the past few years has be artificial intelligence (AI). Everything is AI. That was even before ChatGPT soared into everyone’s attention span.

So, when I hear about a company with a product using Generative AI, I need to be shown.

I wrote about Retrocausal a few months ago. Then I sort of forgot about it. This press release about the company raising another round of financing gave me the excuse to talk with CEO Dr. Zeeshan Zia.

The funding was straightforward. I wanted to know more about what the software does and how Generative AI fits into the picture.

Retrocausal, a leading platform provider for manufacturing process management, today announced a $5.3M financing round co-led by Glasswing Ventures, One Way Ventures, and Indicator Ventures, along with participation from existing investors Argon Ventures, Differential Ventures, Ascend Vietnam Ventures, Incubate Fund US, SaaS Ventures, Hypertherm Ventures, Stage Venture Partners, and Techstars.

One product of the company is Assembly Copilot. This product uses a number of vision and projected video displays to guide workers—especially new, untrained ones—into a proper assembly workflow. It shows what to do next and how to assemble. It will go to the next step without any specific input from the worker. In other words, it does its job and gets out of the way. All of us who have ever developed automation systems know that when the automation gets in the way of the work, then the worker will turn it off.

Funding will be used to meet the increased market demand for its proprietary generative AI technology, Retrocausal’s Kaizen Copilot software for Manufacturing Assembly Optimization. Retrocausal’s solution simplifies manual assembly operations and the underlying processes to empower the low-skilled workforce to take on high-skill manufacturing jobs. 

Kaizen Copilot (check out video) is way cool. An industrial engineer or supervisor sets up a prototype workstation along with a worker. Components are arranged and work instructions defined. A camera, even the one on their smartphone, is installed above with the entire operation in the field of view. The software captures the work—motions, locations, relative positions—and analyzes the process. Kaizen Copilot uses a Generative AI recommendation engine to analyze the workflow, material placements, and worker motions to provide suggestions for better workstation layout and assembly order.

The big problem Retrocausal addresses is the labor crisis in manufacturing. According to the National Association of Manufacturers, by 2030, the manufacturing skills gap, caused by the labor market’s struggle to find workers with highly technical and manual expertise, could lead to 2.1 million unfilled jobs risking over $1 trillion in losses that year alone. 

Retrocausal’s Copilot software allows an untrained worker to become productive on a new process within five minutes and deliver the productivity and quality of someone who has had months of training, resulting in 25% greater First Time Yields (FTY) and 90% less assembly-related scrap costs. 

Retrocausal’s Copilot extends its impact beyond individual performance. It equips production supervisors and junior industrial engineers with the capability to radically overhaul workstation design and re-balance assembly lines. Production supervisors and junior industrial engineers can improve workstation design and re-balance assembly lines to minimize the operator headcount needed to run a line while eliminating bottlenecks leading to 35% greater value per operator.

“We are thrilled to receive the continued support of Glasswing Ventures, new investors One Way Ventures and Indicator Ventures, and our existing investors, in helping us meet the growing demand for our offering,” said Dr. Zeeshan Zia, CEO of Retrocausal. “This latest round will help our team accelerate deployment as we continue to leverage AI to address the manufacturing talent shortage and re-imagine manufacturing assembly processes.”

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