AI Agents for Advanced Time Series Data Analytics : Jeff Tao - CEO and Founder, TDengine
AI in ManufacturingShow Notes
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.
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