Rajesh Iyengar’s manufacturing AI book hits Amazon bestseller list and Times Square

Lincode Labs CEO Rajesh Iyengar’s book on dependable factory AI inspection reached Amazon bestseller status and was featured in New York Times Square. The recognition spotlights a practical approach to making visual inspection work on real production lines, where lighting, defects, and process variation can make or break deployment.

Why it matters: - The book puts a manufacturing problem front and center: how to make AI inspection dependable on a live production line, not just in a demo. - The attention could help more plant leaders, quality teams, and engineering managers evaluate AI projects with clearer expectations for deployment and value. - The book’s core message ties AI success to factory conditions, human expertise, and operational discipline.

What happened: - Lincode Labs CEO Rajesh Iyengar’s book, Teach the Factory to See: How Production, Manufacturing Engineering, and Quality Teams Make AI Inspection Dependable, became an Amazon bestseller. - The book was also featured in New York Times Square. - The book is available on Amazon as Teach the Factory to See. - Nitin Kartik, CEO of Caribou Strategic, helped Iyengar publish the book.

The details: - The book argues that dependable inspection depends on coordinated decisions across people, processes, optics, data, and technology. - Iyengar draws on factory deployment experience to connect AI vision choices with the priorities of production, manufacturing engineering, and quality teams. - The book says a camera capturing an image and a model classifying it are only parts of a larger system that must work under actual factory conditions. - Factory examples focus on lighting, reflections, surface contamination, and changing production conditions. - The book says those variables can make a strong controlled test look much less reliable on the line. - Iyengar says teams should define the inspection requirement before choosing the technology. - That means agreeing on what counts as a defect, what characteristics must be measured, and whether the method can detect the condition that matters. - The book is written for engineering managers, quality leaders, plant managers, operations executives, and supporting teams. - It is designed to be accessible without requiring readers to be data scientists. - The guidance is meant to help teams review projects, discuss vendors, and shape manufacturing strategy.

Between the lines: - The bestseller status gives the book a wider platform, but the bigger story is the effort to make AI adoption more practical for manufacturers. - Iyengar’s framing pushes away from hype and toward implementation details that determine whether a system earns trust. - The book treats missed defects and false rejections as business issues, not just technical errors, because both can affect cost, workflow, and customer confidence. - Human expertise remains central in the book’s view of Industry 5.0, where operators and quality engineers help train, supervise, and improve intelligent systems. - The emphasis on traceability, ownership, and feedback suggests inspection should feed continuous improvement, not only pass-or-fail decisions.

What’s next: - Manufacturers evaluating their first visual-inspection project can use the book as a framework for readiness and acceptance criteria. - Teams with a struggling pilot may use its guidance to identify gaps in process, data, optics, or ownership. - Organizations expanding an existing system across more lines or plants may use the same framework to standardize deployment. - Lincode Labs says it focuses on AI-powered visual inspection solutions for real production environments.

The bottom line: - The book’s momentum reflects growing interest in a harder question than whether AI can inspect a part: whether it can do so reliably in the messy conditions of manufacturing.