Эпизоды
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Industrial teams still rely on fragmented and manual processes to match complex product specs with use-case-specific needs.Take this example:You're selling a vision sensor to a factory. To get it right, you need to know:⇨ What’s the size and speed of the conveyor line?⇨ Is the plant located in Munich or Arizona?⇨ Will this sensor withstand that temperature range?⇨ What PLC is the customer using — Siemens or Rockwell?⇨ Will the sensor integrate without conflict?⇨ Are there newer models in the portfolio that fit better?⇨ Can it be installed without disrupting production?Now imagine trying to answer all of that...⇨ Using PDFs.⇨Email chains.⇨ Gut instinct.⇨ And hoping Bob from Engineering isn’t on vacation.With an AI Agents trained on your connected industrial knowledge:✅ All technical documentation, manuals, spec sheets, CAD drawings, becomes queryable✅ Reps and engineers can ask natural-language questions and get verified answers✅ Compliance, compatibility, and environmental fit can be checked in seconds✅ Human experts stay in the loop, but no longer stuck in the weedsI recently sat down with Fay Goldstein Co-Founder and CEO of Folio to discuss the application of AI Agents for Industrial Sales and Application Engineers.
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 🌐 Website: https://www.folio.build/ 💼 LinkedIn: https://www.linkedin.com/in/faygoldstein/
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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.Most businesses lack tools to close the loop between:⇨ Data collection⇨ AI training⇨ Deployment⇨ Continuous retraining⇨ Business impact validationAnd they struggle to connect domain experts with data scientists. To learn more about building and scaling closed-loop AI for industrial operations I recently sat down with Dr. Nikita Golovko who is a Software Architect for Industrial AI at Siemens.
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Пропущенные эпизоды?
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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.
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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
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In manufacturing, time-series data is everywhere, but most plants are still relying on static dashboards, lagging insights, and manual root-cause analysis.The result?- Downtime that’s explained, not prevented- Insights that arrive, after the line slows down- Human effort wasted on repeat investigationsAI agents transform the way manufacturers harness time-series data. They process live sensor feeds while simultaneously referencing historical records, enabling instant anomaly detection and context-aware decisions.They can correlate vast time-series data with external factors to uncover insights missed by rigid statistical models.They can trigger actions like maintenance tickets or production adjustments directly from analytics, bypassing manual interpretation steps.They connect the dots across thousands of data streams in real time, automatically identifying root causes and recommending actions on the fly.In the latest episode of the AI in Manufacturing podcast, I sat down with Jeff Tao to learn more about the application of AI Agents for Advanced Time Series Data Analytics. Jeff is the CEO and Founder of TDengine, the developers of TDengine an IIoT time-series database, TDgpt time-series AI Agent, and TDtsfm, a Time-Series Foundation Model.
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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:
Core Challenges in Managing Industrial Data for Data‑Driven ManufacturingThe Role of Ontologies and Knowledge Graphs in Advancing Industrial Data Interoperability and AnalyticsIIoT Data Integration and Standardization Approaches Semantic‑Modeling Best Practices for Scaling Value CreationUsing Knowledge Graphs as Infrastructure for Digital Twins and Industrial AIIndustrial AI Use Cases Powered by Knowledge GraphsThe Real Business Value of Digital Twins in ManufacturingBuilding the Next-Gen Digital Twins with AI, LLMs, and Knowledge GraphsAI Agents, and MCP for Distributed Intelligence on Digital TwinsMulti-Agent AI Systems for the Future of Manufacturing Digitalization -
As manufacturing demands increase, integrating AI-powered visual systems into quality inspection processes becomes increasingly beneficial.
While traditional inspection methods have been the cornerstone of quality control in manufacturing, they come with limitations such as subjectivity, fatigue, and scalability challenges.
AI-powered visual inspection systems address these issues.
Leveraging advanced algorithms and machine‑learning models, they analyze images with high accuracy, identifying defects that may be invisible to the human eye.
This not only enhances the reliability of quality assessments but also increases operational efficiency, allowing manufacturers to streamline their processes and reduce costs.
The capability to detect anomalies in real-time empowers companies to address issues before they escalate, ensuring that only the highest-quality components progress through production.
To find out more about the application of Visual AI Inspection in manufacturing, I recently sat down with Priyansha Bagaria who is the Founder and CEO of Loopr AI.
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Modern manufacturing environments generate a staggering amount of data from machines, processes, quality checks, logistics, and inventory. And yet, most of it goes unseen, unused, and unanalyzed.
Why?
Because the data is too vast, too fast, and too fragmented for any human to handle in real-time.
Even the best engineers can’t monitor thousands of variables 24/7.
And failing to harness this data has real consequences. Critical warning signs of equipment problems or process inefficiencies can be missed, leading to unplanned downtime and quality issues.
The biggest challenge AI Agents solve in industrial enterprises is transforming this overwhelming amount of complex data into actionable intelligence.
However, AI Agents are only powerful for manufacturing data analytics when paired with the right context.
That means feeding them, sensor data, maintenance logs, ERP & MES records, operator notes, engineering drawings, and SOP documents e.t.c. And quickly surfacing the most relevant information to power rapid AI-driven decision-making.
This is where Vector Storage and Search comes into play.
To learn more about Vector Databases and Data Structure for Industrial AI Agents I had a chat with Humza Akhtar, PhD who is the Senior Industry Principal for Manufacturing and Automotive at MongoDB.
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Every minute a machine is offline costs money. That’s why Mean Time to Repair (MTTR) is one of the most vital metrics in manufacturing. It tells you how fast your team can identify an issue, find the solution, and get the line moving again.Unfortunately, in many facilities, this process is slow and cumbersome: when a technician sees an error code, they often have to sift through hundreds of pages of documentation while the clock is ticking.A long MTTR doesn’t just mean downtime; it means:- Lost production- Missed delivery deadlines- Heightened stress on frontline teams- Frustration for leadership and customersBy using Generative AI to access your entire library of manuals, maintenance logs, and SOPs, maintenance teams can quickly find the answers they need and take swift action to minimize downtime.To learn more about Reducing Machine Downtime with AI-Powered Knowledge Management I had a chat with Jose Dos Santos, Co-Founder and CEO of Industrial AI
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For decades, manufacturers have relied on traditional analytics—correlations, trendlines, dashboards—to make operational decisions. But there's a limit:
Correlation ≠ Causation
Just because two variables move together doesn’t mean one causes the other.
This blind spot can lead to poor decisions and surface-level fixes that don’t solve the real issue.
For example, a machine’s temperature spikes often coincide with defects. Traditional analytics might alert you when it happens—but not why. Is it the temperature? A faulty sensor? Operator error?
Causal Inference flips the script. Instead of just observing data patterns, it asks:
“What actually caused this outcome?”
I recently sat down with Daniele Gamba, CEO of AISent Srl to learn more about building industrial intelligence solutions with Caussal AI.
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Manufacturing leaders are familiar with physical waste; scrap, rework, and inefficiencies in production. But digital waste is the hidden inefficiency that’s just as costly. It includes:𝐔𝐧𝐮𝐬𝐞𝐝 𝐃𝐚𝐭𝐚: Factories generate massive amounts of data, but much of it is never analyzed or leveraged for decision-making.𝐈𝐧𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐃𝐚𝐭𝐚 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠: Engineers waste time manually entering, cleaning, or searching for information that should be automated.𝐒𝐢𝐥𝐨𝐞𝐝 𝐈𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧: Key insights are trapped in different departments or legacy systems, preventing AI-driven optimization.Digital waste silently drains resources, increasing operational costs while blocking AI from delivering its full potential.Once manufacturers recognize digital waste, the next step is identifying where AI can generate the biggest returns. To learn more about finding opportunities for the application of AI in manufacturing, I recently sat down with Patrick Byrne, Co-Founder and CEO of Annora AI.
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Manufacturers are constantly battling two critical challenges:
Inefficiencies in Equipment Usage: Downtime, slow cycle times, and unidentified bottlenecks reduce Overall Equipment Effectiveness (OEE), leading to wasted resources and missed production targets.
Safety Risks: Ensuring worker safety while maintaining productivity is difficult, especially in environments with heavy machinery and fast-moving processes.
Despite best efforts, traditional methods struggle to keep up with the complexity and speed of modern manufacturing.
By using computer vision and deep learning, Video AI Agents bring continuous, detection and response of issues—far beyond what traditional methods alone can achieve.
I recently sat down with Karim Saleh, Co-founder and CEO at Cerrion to learn more about how to Maximize OEE and production Line Safety with Video AI Agents
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AI’s success in manufacturing depends on the ability to seamlessly integrate data from machines and systems across the factory floor and supply chain.
Without strong connectivity, AI remains underutilized, limited by data silos, and inconsistent integration.
Connectivity isn’t just about linking devices; it’s about creating a unified data environment where AI can operate at its full potential—powering everything from predictive maintenance to automated quality control and beyond.
To learn more about IT/OT connectivity for enabling AI use cases in manufacturing I had a conversation with Bernd Hafenrichter who is the CTO of soffico GmbH.
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Frontline workers are the backbone of manufacturing, but they’re often held back by manual data entry, process inefficiencies, and knowledge gaps. AI-powered Industrial Copilots offer a solution that elevates their capabilities:𝐍𝐨 𝐌𝐨𝐫𝐞 𝐌𝐚𝐧𝐮𝐚𝐥 𝐃𝐚𝐭𝐚 𝐄𝐧𝐭𝐫𝐲AI Copilots automate data capture and seamlessly integrate with existing systems—eliminating wasted time and inaccuracies.𝐒𝐦𝐚𝐫𝐭𝐞𝐫, 𝐅𝐚𝐬𝐭𝐞𝐫 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬AI surfaces real-time insights, helping teams reduce downtime, optimize production, and make data-driven decisions on the fly.𝐂𝐥𝐨𝐬𝐢𝐧𝐠 𝐭𝐡𝐞 𝐄𝐱𝐩𝐞𝐫𝐭𝐢𝐬𝐞 𝐆𝐚𝐩AI-driven step-by-step guides provide instant troubleshooting and best practices, ensuring even new employees perform like seasoned experts.𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬As operations grow, AI Copilots adapt to new processes, machinery, and industries, ensuring a future-proofed approach to efficiency and innovation.To learn more about the application of AI Copilots for enhancing Frontline Operations in Manufacturing, I had a chat with Mason Glidden Chief Product and Engineering Officer at Tulip Interfaces.
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Many factories today grapple with recurring production issues and inefficiencies; whether it’s inconsistent quality, unpredictable downtime, or process bottlenecks.The cost of inefficiencies keeps mounting, and while human intuition and manual checks have been valuable tools, they’re no longer enough to drive significant breakthroughs.AI offers an opportunity to uncover hidden patterns that human teams might miss. For instance:- By analyzing machine sensor data, AI can trace yield drops to subtle temperature fluctuations.- AI can identify bad material batches from suppliers or reveal operational bottlenecks.- Instead of vague reports, AI delivers precise, actionable insights, helping teams shift from guesswork to targeted, data-driven solutions.To learn more about how Manufacturers can achieve operational excellence through data-driven manufacturing optimisation with AI, I had a conversation with Zhitao(Steven) Gao who is the CEO and Co-Founder of eXlens.ai.
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While the promise of AI is immense, many manufacturers find themselves stuck in pilot projects, unable to unlock its full potential.
The key lies in addressing foundational challenges and adopting a clear, phased strategy to transform operations.
Fundamentally, AI offers manufacturers a pathway to achieving operational excellence by moving through the four stages of analytics maturity:
1️⃣ Descriptive Analytics – Understanding what happened.
2️⃣ Diagnostic Analytics – Pinpointing root causes.
3️⃣ Predictive Analytics – Forecasting potential equipment failures or quality issues.
4️⃣ Prescriptive Analytics – Recommending the best actions to address challenges.
Despite its promise, many manufacturers struggle with significant obstacles, which include data fragmentation.
I recently had a sit down with Andrew Scheuermann the CEO and Co-Founder of Arch Systems to discuss why building a comprehensive Digital Twin is the key to overcoming these barriers and how manufacturers can use AI to enhance manufacturing workflow efficiency.
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In our latest episode of the AI in Manufacturing Podcast, I sat down with Zeeshan Zia, co-founder and CEO of Retrocausal, to dive deep into how AI co-pilots are transforming the manufacturing sector. Here are three key takeaways:
1️⃣ Labor Challenges Meet Smart Solutions
Manufacturers face critical labor shortages, resulting in significant costs. Zeeshan shared how AI-powered Assembly Co-Pilots are slashing error rates and scrap costs by up to 90% while empowering workers with real-time guidance.2️⃣ Merging Lean Principles with AI
Traditional lean manufacturing focuses on quality, productivity, and safety. RetroCausal’s tools like Kaizen Co-Pilot and Ergo Co-Pilot seamlessly integrate lean methodologies with advanced AI, accelerating time studies and ergonomic assessments in hours instead of weeks.3️⃣ Scalability Across Diverse Workflows
From discrete manufacturing to medical devices, AI co-pilots are not just for single processes—they scale efficiently across multiple sites, even in highly regulated industries. -
Today's manufacturing industry faces significant challenges in managing its data environment.
Vast amounts of unorganized data collected from various sources often become "data swamps," making it difficult to extract meaningful insights and generate value.
This overwhelming complexity hinders decision-making and slows down innovation.
Additionally, the analytics tools currently available are often too complex and static for domain experts to use effectively, leaving them without the critical insights needed to improve processes, optimize production, and make informed decisions.
AI assistants offer a promising solution by bridging the gap between complex data sets and user-friendly interfaces.
They transform unstructured data into actionable insights accessible to everyone in the organization.
To learn more about the application of AI assistants for advanced manufacturing data analytics, I sat down with Stefan Suwelack, the CEO and Co-Founder of Renumics.
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