Many companies waste a significant amount of time navigating complex and unclear files.
For this reason, the need to access data quickly and easily is becoming increasingly evident.
The Zerynth Case History
Zerynth is an Italian deep-tech company that develops an IIoT platform for the rapid and scalable digitalization of industrial production. The company is currently evolving its solution into an Industrial AI Copilot platform, equipped with a conversational agent that enables natural interaction with production data and processes. The development of this artificial intelligence component is considered strategic for the evolution of the product and the company’s future positioning.
The Challenge: The Data Exists, but It’s Hard to Query
Modern factories are gold mines of data. IoT sensors capture every vibration, energy consumption pattern, and production cycle. However, this information often remains “silent” or accessible only to those who know how to interpret complex software tools.
There is also the issue of documentation. User manuals, standard operating procedures (SOPs), and technical guides are often confined to static PDFs hundreds of pages long, making them difficult to consult when information is urgently needed.
The Solution: An Agentic AI and RAG-Based Copilot
At the core of the project is an advanced digital assistant that does not simply “answer” questions but actually reasonsover the data. Unlike traditional chatbots, the system relies on an Agentic RAG (Retrieval-Augmented Generation) architecture.
In practical terms, this means:
- Real-time data access: The system directly queries the platform’s APIs to retrieve KPIs related to machines, energy consumption, and efficiency (OEE).
- Intelligent document consultation: Through a vector database, the Copilot “reads” PDF manuals and provides precise answers, citing the exact source and page.
- Memory and context: The agent remembers the conversation. If a user asks “Compare it with yesterday”, the system understands exactly which machine or metric is being referenced.
“Made in Europe” Technology and Data Security
The experimentation placed strong emphasis on data sovereignty. The core engine relies on Azure OpenAI models to ensure compliance with European regionalization and data privacy requirements, orchestrated through advanced frameworks such as Agno and LangGraph.
One of the system’s key strengths is Explainable AI (XAI). Every generated response is not a “black box”: it includes transparent references to the content used to formulate the answer, ensuring that technicians can always verify the accuracy of the information provided.
Results from the Field: From KPIs to Maintenance
During testing, the integration of the platform enabled several critical use cases:
- KPI analysis: Immediate responses on availability, performance, and quality across production lines.
- Technical support: Automatic opening of support tickets via chat when anomalies are detected.
- Energy efficiency: Comparative analysis of energy consumption across different periods to identify inefficiencies.
The results are significant. The ability to reduce machine downtime (up to 70% in optimized contexts) and drastically cut the time required to find technical information makes AI an essential tool for the competitiveness of manufacturing SMEs.
Key Takeaways for Companies
Adopting an Industrial AI Copilot is not just a technological upgrade, it represents a real paradigm shift:
- Data democratization: Information becomes accessible to everyone, not only IT specialists.
- Faster decision-making: Decisions based on reliable data obtained in seconds.
- Reduced human error: Guided procedures and immediate access to technical documentation.
AI-MATTERS demonstrates that artificial intelligence in the factory does not need to be complex. In collaboration with Zerynth, we have built a bridge between humans and machines, where natural language becomes the remote control of production.
A truly smart factory is one that can answer the questions of its operators. And today, it has finally started to do so.
Why AI-MATTERS?
AI-MATTERS enables companies to:
- Test AI solutions in real-world manufacturing enivronments
- Access advanced infrastructure and expertise
- Validate performance before scaling or investing
By bridging the gap between prototype and deployment, AI-MATTERS helps companies move from experimentation to industrial implementation with reduced risk.
FAQs
AI copilots use natural language interfaces to make production data accessible to non-experts, allowering operators and engineers to query complex datasets without needing specialized tools. This helps democratize data and improve decision-making across teams.
For more information and a specific dive into your use case, we welcome you to contact us for a non-binding conversation.
Validation requires testing the AI Copilot in real production environments using live data and real use cases. This ensures the system can handle operational complexity, integrat3e with existing systems, and deliver reliable insights before scaling.
AI copilots improve:
- decision-making speed
- operational efficiency
- machine uptime
- data-driven insights
They can significantly reduce downtime and improve performance by enabling real-time analysis and faster troubleshooting.
Natural language interfaces allow users to interact with machines, data, and documentation more intuitively. Instead of navigating complex dashboards, users can simply ask questions and receive actionable insights, reducing time spent searching for information.
Explainable AI (XAI) ensures that every recommendation or answer can be traced back to its data source. This builds trust among engineers and operators and is critical for decision-making in safety-critical environments.
Would you like to know how our project managers can help your organisation?
Contact us for a non-binding conversation: https://ai-matters.eu/contact/