DETROIT, MI

Rajesh Iyengar, CEO of Lincode Labs, authored Teach the Factory to See: How Production, Manufacturing Engineering, and Quality Teams Make AI Inspection Dependable, has reached Amazon bestseller status and has been featured in Times Square in New York City.

The milestones bring attention to a practical challenge facing manufacturers adopting artificial intelligence: how can an AI visual-inspection system move from a successful demonstration to dependable performance on an actual production line?

Drawing on his experience in technology, entrepreneurship, and factory deployment, Iyengar's book examines the technical and operational decisions involved in implementing AI-powered visual inspection. Its central premise is that dependable inspection requires more than an AI model or camera. People, processes, optics, data, and technology all have to work together to support consistent decisions under real production conditions.

From AI Demonstration to Production Reality

An AI inspection system can perform successfully under controlled testing conditions and still face challenges when introduced to a working factory.

In Teach the Factory to See, Iyengar examines how factors including lighting, reflections, surface contamination, and changing production conditions can affect inspection performance. An approach that appears promising during a controlled test may require additional engineering and operational preparation before it can be deployed consistently on a production line.

The book approaches manufacturing AI from the perspective of the teams responsible for putting the technology into operation.

Rather than treating visual inspection simply as a matter of training a model to classify images, Iyengar examines how inspection requirements, factory conditions, technical infrastructure, and human decision-making interact.

Defining the Inspection Problem Before Choosing the Technology

A central part of Iyengar's framework is determining what the inspection system actually needs to identify before selecting the technology intended to perform the task.

Manufacturing teams need to establish what constitutes a defect, which characteristics need to be measured, and whether the proposed inspection method can detect the condition that matters.

Defining these requirements provides a basis for evaluating technical proposals and establishing meaningful acceptance criteria before a system is deployed.

This approach gives manufacturing leaders a framework for evaluating AI inspection projects according to their intended production requirements rather than relying solely on demonstrations or model-performance claims.

Inspection Errors Become Manufacturing Costs

AI inspection decisions can have consequences beyond the inspection station itself.

A false rejection can result in unnecessary handling, reinspection, or scrap when a good part is classified as defective. An undetected defect, meanwhile, can generate additional costs as it moves further through production or reaches a customer.

Iyengar connects these inspection outcomes with broader manufacturing concerns, including workflow, production costs, quality control, and customer confidence.

This perspective places visual inspection within the larger economics of manufacturing rather than treating inspection accuracy as an isolated technical measurement.

Human Expertise Remains Part of Manufacturing AI

Human expertise is another central theme of Teach the Factory to See.

The book emphasizes the knowledge of operators and quality engineers in teaching, supervising, and improving manufacturing AI. Their understanding of acceptable variation, production conditions, recurring problems, and practical factory requirements can contribute to the development and ongoing improvement of inspection systems.

Iyengar's vision of Industry 5.0 places human judgment within technological progress, examining how intelligent systems can work alongside workforce expertise.

Production, manufacturing engineering, quality, and technology teams each bring different responsibilities to an AI inspection project. Coordinating those responsibilities can help integrate inspection technology into everyday manufacturing operations.

From Inspection Results to Continuous Improvement

The role of AI inspection does not necessarily end with a pass-or-fail decision.

Iyengar also examines how traceability, clear ownership, and inspection feedback can help organizations investigate recurring production problems and guide corrective action.

This connects visual inspection with a broader objective: building manufacturing operations that can learn from production experience.

When inspection information is connected with production processes and corrective-action workflows, organizations can use that information to investigate recurring issues rather than treating individual inspection results as isolated events.

A Practical Framework for Manufacturing Leaders

Teach the Factory to See is written for engineering managers, quality leaders, plant managers, operations executives, and teams supporting manufacturing technology.

The book addresses organizations at different stages of AI visual-inspection adoption, including companies evaluating their first visual-inspection project, addressing a struggling pilot, or preparing to expand an established system across additional production lines and plants.

The book makes complex implementation questions accessible without requiring readers to be data scientists. Its framework can be brought into project reviews, vendor discussions, and manufacturing strategy conversations.

Amazon Bestseller Status and Times Square Feature

The book's Amazon bestseller status and Times Square feature have increased visibility for Iyengar's perspective on the practical implementation of artificial intelligence in manufacturing.

The milestones also bring attention to a specialized area of industrial technology: how AI-powered visual inspection can be developed and deployed to address real production and quality requirements.

A video documenting the book's Amazon bestseller milestone is available here, while footage of the Times Square feature is available here.

Teach the Factory to See: How Production, Manufacturing Engineering, and Quality Teams Make AI Inspection Dependable is available on Amazon.

About Rajesh Iyengar

Rajesh Iyengar is CEO of Lincode Labs, an AI visual-inspection company focused on manufacturing quality.

Iyengar's work brings together experience in technology, entrepreneurship, and factory deployment. His book examines how manufacturing organizations can connect AI capabilities with production requirements and the practical realities of factory operations.

About Lincode Labs

Lincode Labs develops AI-powered visual-inspection solutions designed to help manufacturers detect defects and strengthen quality control.

Led by CEO Rajesh Iyengar, Lincode Labs brings together artificial intelligence, machine vision, and manufacturing expertise to address inspection challenges in real production environments. The company works with manufacturing teams to connect technology with operational needs, supporting more consistent inspection decisions and helping people apply their expertise to improving factory performance.

Website: Lincode.ai

Book Availability

Teach the Factory to See: How Production, Manufacturing Engineering, and Quality Teams Make AI Inspection Dependable is available on Amazon.

Media Contact Details

Denise Baltazar

Lincode Labs

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