January 23, 2026

Beyond IoT - the Semantic Digital Twin is here

Semantic Digital Twins are a reality today. Sometimes called physical AI or industrial AI, the underlying concept is the same: the evolution of Digital Twins.

Asier Galdos
CEO

With the breakneck rise of AI tools and available software capabilities, Semantic Digital Twins are a reality today. Sometimes called physical AI or industrial AI, the underlying concept is the same: the evolution of Digital Twins, using language and logic (hence the “semantic” qualifier) as a query interface.

Let’s unpack the beast.

Semantic Digital Twin is a unified platform merging real-time data (coming from physical devices and other data sources such as ERPs) with predictions, documentation and control into one queryable experience, by using natural language.

The Semantic Digital Twin is the ideal complement and strategic evolution of IoT platforms. While IoT platforms ensure robust data capture, advanced connectivity and efficient real-time asset management, the Semantic Digital Twin adds a layer of contextual intelligence and 3D visualization.

By virtue of this, the user can interact with assets using natural language, querying both historical telemetry and linked technical documentation. Ultimately, the Semantic Digital Twin is an expert system capable of predicting future behavior and intuitively optimizing industrial processes.

What’s in it for you? 

Semantic Digital Twins remove technical barriers through natural language queries, allowing instant access to historical data, technical manuals and real-time status, facilitating faster troubleshooting and preserving "know-how" in the company, even as people come, go or retire.

Major benefits include:

  • Semantic: By being "semantic," you don't just store data, you define what it means and how it relates to everything else. 
  • Generative AI/RAG: You can query technical documentation (PDFs, manuals) using natural language.
  • Temporal intelligence (Past):  You can "rewind" the Twin’s state to a specific date and time to investigate incidents.
  • Predictive simulation (Future): By applying machine learning models to real-time data, the digital twin can "fast-forward" to predict potential outcomes. 

Data and technological sovereignty is critical in these uncertain times, and companies are opting for software systems that are infrastructure and model agnostic. 

Biotz has always had companies' independence in mind, creating tools and products with reduced technical debt and with total control of its underlying technology. This is the way Europe must be moving towards and we are happy to be part of this vital shift.

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