CDOT AI Code - A New Language for Parts: Serra Tuzcuoglu CEO and Co Founder, Cosmodot - CDOT AI Code
AI in ManufacturingShow Notes
Part traceability in manufacturing has long relied on traditional barcodes that fail where it matters most: under heat, blasting, and coating, e.t.c.
As a result, manufacturers normally place barcodes after key part transformations.
That means, for 70%+ of the production process, you're flying blind. You're guessing which parts went through which treatments.
And when something fails? You're looking at massive recalls, supplier penalties, and lost time.
What if we could code physical parts in a way that never fades?
That’s exactly what Serra Tuzcuoglu and her Co-Founder invented. CDOT AI Code, a frequency-based, AI-readable identifier that can survive the harshest industrial conditions.
Unlike traditional visual patterns, it uses signal recognition instead of contrast, enabling it to be read even when surfaces are scratched, painted, or distorted.
A global appliance manufacturer gave them a challenge: “If your code can survive enamelling and high heat, we’ll use it.”
It did. That was the beginning. CDOT AI Code Code is now deployed globally, from Renault crankshafts to Ford EV battery trays, military tanks to printing cylinders.
In the latest episode of the AI in Manufacturing podcast, I sat down with Serra Tuzcuoglu, the CEO and Co-Founder of COSMODOT, to discuss:
✅ How CDOT AI Code works
✅ Solution Architecture & Integration into the factory network
✅ Readiness for AI-based quality analytics and creation of digital twins.
✅ Real-world examples and Case Studies
More from AI in Manufacturing
AI Is Reshaping Industrial Software. Who Wins? | Michael Finocchiaro Will AI simply make established industrial software better, or will it fundamentally change which companies dominate the market? In this episode of AI in Manufacturing, I sit down with Michael Finocchiaro, founder of ThreadMoat Intelligence, to explore how AI is reshaping the competitive landscape of industrial technology, where AI-native startups are challenging established vendors, and what the industrial software market could look like over the next five years. We go beyond the competitive landscape to examine how the underlying technology architecture is evolving, from increasingly composable software and AI-driven orchestration to the changing roles of traditional systems like MES and PLM. In this episode, we explore: Why AI-native startups are challenging established industrial software vendors and where incumbents still have an advantage. How AI is changing traditional industrial software categories, including MES, PLM, and manufacturing data platforms. Why composable architectures and AI agents could fundamentally change how industrial systems are integrated and orchestrated. How manufacturers should think about data ownership, interoperability, and vendor lock-in when evaluating emerging technologies. What the industrial software landscape could look like in five years, including the evolving roles of AI agents, knowledge graphs, and human decision-making. Michael brings a broad perspective from tracking industrial technology startups, interviewing founders, and analyzing emerging trends across engineering and manufacturing software. Whether you're building industrial software, investing in emerging technologies, or shaping your organization's manufacturing technology strategy, this conversation offers valuable perspectives on the shifts already underway and what they could mean for the industry. Learn more: Industry40.tv: https://www.industry40.tv
# AI in Manufacturing Podcast
# AI in Manufacturing Podcast — Show Notes ## Episode: Scaling Industrial Intelligence with the I3X Common API
# AI in Manufacturing Podcast: Episode Show Notes
**Podcast Name:** AI in Manufacturing Podcast **Episode Title:** How to Build AI Solutions That Actually Work on the Factory Floor **Guest:** Renan De Villiers, Founder & CEO, OSS Ventures **Host:** Kudzai Manditereza