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• AI Agents for Advanced Time Series Data Analytics : Jeff Tao - CEO and Founder, TDengine

  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 investigations AI 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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• Powering Industrial AI and Digital Twin Use Cases with Knowledge Graphs : João Dias-Ferreira - Head of AI, Knowledge Graphs and IoT, SCANIA

  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 Manufacturing The Role of Ontologies and Knowledge Graphs in Advancing Industrial Data Interoperability and Analytics IIoT Data Integration and Standardization Approaches  Semantic‑Modeling Best Practices for Scaling Value Creation Using Knowledge Graphs as Infrastructure for Digital Twins and Industrial AI Industrial AI Use Cases Powered by Knowledge Graphs The Real Business Value of Digital Twins in Manufacturing Building the Next-Gen Digital Twins with AI, LLMs, and Knowledge Graphs AI Agents, and MCP for Distributed Intelligence on Digital Twins Multi-Agent AI Systems for the Future of Manufacturing Digitalization

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• Real-Time Quality Control Using AI-Powered Visual Inspection : Priyansha Bagaria, PhD -Founder and CEO, Loopr AI

  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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• Vector Databases and Data Structure for Industrial AI Agents : Humza Akhtar, PhD - Senior Industry Principal - Manufacturing and Automotive, MongoDB

  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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• Industrial Machine Downtime Reduction Using Generative AI : Jose Dos Santos - Co-founder & CEO, Industrial AI

  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 customers By 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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