Many companies waste a lot of time navigating complex and unclear files.
For this reason, the need to be able to retrieve data quickly and easily is becoming greater and clearer.
The Zerynth Case
Zerynth is an Italian deep-tech company developing an IIoT platform for the rapid and scalable digitization of industrial manufacturing. It is currently evolving its solution into an “Industrial AI Copilot” platform equipped with a conversational agent for natural interaction with data and production processes. The development of this artificial intelligence component is considered strategic for the company’s product evolution and future positioning.
The challenge: the data are there, but they are hard to query
Modern factories are data gold mines. IoT sensors capture every vibration, energy consumption and production cycle. However, this information often remains “silent” or accessible only to those who can interpret complex software.
Added to this is the problem of documentation: user manuals, operating procedures (SOPs) and technical guides are often confined to static PDFs of hundreds of pages, which are difficult to consult in times of need.
The Solution: A Copilot based on Agentic AI and RAG.
At the heart of the project is an evolved digital assistant that not only “answers” but also “reasons” about the data. Unlike traditional chatbots, this system uses an Agentic RAG (Retrieval-Augmented Generation) architecture.
What does it mean in practical terms?
- Real-time data access: The system directly queries the platform API to retrieve KPIs on machinery, consumption and efficiency (OEE).
- Intelligent document consultation: Through a vector database, Copilot “reads” PDF manuals and provides accurate answers, citing the source and the exact page.
- Memory and Context: The agent remembers the conversation. If you ask , “Compare it to yesterday,” he knows exactly which car or value you are referring to.
“Made in Europe” Technology and Data Security
The experimentation has focused heavily on data sovereignty. The core engine is based on Azure OpenAI models to ensure compliance with European regionalization (EU data privacy), orchestrated through advanced frameworks such as Agno and LangGraph.
One of the strengths is XAI (Explainable AI): each response generated is not a “black box,” but includes transparent references to the content used to formulate it, ensuring that the technician can always verify the accuracy of the information provided.
Results from the field: from KPIs to maintenance
During testing, platform integration enabled critical use cases to be covered:
- KPI analysis: Immediate answers on line availability, performance, and quality.
- Technical Support: Automatically opening support tickets via chat when the system detects anomalies.
- Energy Efficiency: Comparative analysis of consumption between different periods to identify waste.
The numbers speak for themselves: the ability to reduce downtime (by up to 70 percent in optimized contexts) and drastically cut the time it takes to search for technical information makes AI an indispensable tool for the competitiveness of manufacturing SMEs.
Takeaways for businesses
The adoption of an Industrial AI Copilot is not just a technology upgrade, but a paradigm shift:
- Democratization of data: Information is available to everyone, not just IT experts.
- Decision-making speed: Decisions based on firm data, obtained in seconds.
- Reducing human error: Wizards and quick access to instruction manuals.
The AI-MATTERS project proves that artificial intelligence in the factory does not have to be complicated. In collaboration with Zerynth, we have created a bridge between man and machine, where natural language becomes the remote control of production.
The smart factory is the one that can answer the questions of its operators. And today, it has finally begun to do so.