Instead of sending data to the cloud for processing, Edge AI analyzes data right where it’s generated, on the machine, in the plant, in real time.

The first foundation model purpose-built for refining and petrochemicals?

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

Learn how Jonathan and his team at Albemarle Corp went from pilots to $150M in annual improvements through a business-first, scalable AI strategy.

Industrial teams still rely on fragmented and manual processes to match complex product specs with use-case-specific needs.

ABOUT FOLIO:

Folio’s AI platform empowers industrial sales and application engineers by turning technical specs, configuration data, and application info into instant answers, recommendations, and agentic workflows, speeding work, cutting errors, and boosting revenue for industrial manufacturers and distributors. Learn more at www.folio.build

ABOUT FAY:

Fay Goldstein is the Co-Founder and CEO of Folio, an AI-powered platform that transforms how manufacturers and distributors sell and support complex and technical industrial product portfolios. Before founding Folio, she spent her summers managing direct and online sales at local automotive AC condenser and compressor shop, led strategic GTM and communications at an automotive telematics data company, and worked at an early-stage venture capital firm, where she supported dozens of early-stage startups on their initial GTM and communication strategies. Fay graduated magna cum laude from Florida International University and holds an MBA from Reichman University.

CONNECT WITH FAY

In theory, AI should learn, adapt, and improve continuously. But in reality, most deployments are static and disconnected from the evolving complexity of shop floor operations.

Many small and mid-sized manufacturers want to explore AI to improve efficiency, reduce waste, or make their processes smarter.

However, this process requires OT and IT knowledge not present in many

industrial companies, mainly SMEs.

Ander Garcia Gangoiti and his team built a micro-service edge architecture based on MQTT, TimescaleDB, Node-Red and Grafana stack to ease the integration of soft AI models into industrial system.

The architecture has been successfully validated controlling the vacuum

generation process of an industrial machine.

Soft AI models applied to real-time data of the machine analyze the vacuum value to decide when the most suitable time is:

⇨ to start the second pump of the machine,

⇨ to finish the process, and

⇨ to stop the process due to the detection of humidity.

Ander is the Director of Data Intelligence for Industry at Vicomtech and I recently sat down with him on the AI in Manufacturing podcast.

Imagine a control system that learns, optimizes in real-time, and integrates seamlessly with both field assets and cloud-native AI platforms. 

This is the next chapter of industrial process automation.

Already implemented at the largest Oil refinery in the world, Software-defined control systems break the traditional link between hardware and logic.

This separation allows for dynamic control, centralized intelligence, and flexible deployment across complex industrial environments.

When integrated with time-series foundation models, these systems harness AI for intelligent loop control, advanced process optimization, and even reinforcement learning, driving unprecedented levels of performance in control environments.

In the latest episode of the AI in Manufacturing podcast, I sat down with Huize Zhang to explore this transformation. Huize is the Vice President at SUPCON, China’s leading DCS provider, and the founder of FREEZONEX, an open-source IIoT platform.

Here’s the outline of our conversation:

-The Control Platform of The Future

-Open Standards and Platforms

-AI-Driven Optimization in Process Industries

-Time-Series Pre-Trained Transformers

-Reinforcement Learning in Process Industries

-UNS Integration with AI Agents

In manufacturing, time-series data is everywhere, but most plants are still relying on static dashboards, lagging insights, and manual root-cause analysis.

Learn how Joao and and team are using Knowledge Graphs and IIoT to power Industrial AI and Digital Twin use cases at Scania.

Here’s the outline of our conversation: