SAN JOSE, Calif.–(BUSINESS WIRE)–Aidentyx today announced the official launch of the next generation of its PODO® AI Framework, an agentic AI platform that turns complex industrial data into clear insights, confident decisions, and faster action on the plant floor.
Companies have invested heavily in data systems, dashboards, and predictive models, yet without ties to how their plants actually operate, teams often cannot act on AI recommendations with full confidence and struggle even more to scale them. This release addresses this persistent challenge in manufacturing AI.
This generation of PODO® closes that gap, unifying plant knowledge, purpose-built AI applications, a library of AI skills and tools, and AI Engineers in a single platform built for industrial environments.
“Manufacturing AI has to understand the plant it’s running in, not just the data it’s fed,” said Jason Kim, CEO of Aidentyx. “This generation of PODO® finally gives it that foundation.”
At the core of the release is the knowledge graph powering PODO®: a live map of each plant’s assets, processes, people, and priorities. Manufacturing problems rarely have a single cause or a contained impact. Instead of treating every data point on its own, PODO®’s knowledge graph traces both root cause and downstream business impact, so recommendations reflect the full picture instead of a single data point.
That same knowledge graph is what equips PODO®’s AI agents to work as AI Engineers. Combined with Aidentyx’s library of AI skills and tools, they answer questions, analyze plant context, recommend actions, and coordinate next steps with the team on daily manufacturing work.
PODO® is already running in production across semiconductor, food & beverage, water utilities, and steel manufacturing, proving that agentic AI delivers real results in some of the most demanding industrial environments.
About Aidentyx
Aidentyx is a US-based company providing agentic AI solutions for manufacturers. Through PODO®, Aidentyx equips manufacturers to detect, diagnose, and act across equipment performance, process optimization, energy, quality, and operational decision-making.
Recommended For You:
Agentic AI Security Risks: What Enterprises Must Prepare For
