How Live Digital Twin Learned to Talk to Humans – and What This Changes in Building Operations As live digital twins evolve, a building ceases to be merely a collection of observable parameters and becomes a cognitive system - capable of understanding its own condition, explaining it, learning from operational experience, and continuously refining its behaviour. The logical continuation of this evolution is the ability to speak to humans in a human language.
They can now explain their behaviour in a form that is understandable to people - not through monitoring dashboards, charts, and reports, but through dialogue. A person asks a question and receives an answer: why energy consumption has increased, why the system is operating unstably, or what consequences a particular decision may entail. At the same time, the dialogue may be initiated not only by the user, but also by the system itself - in situations where the building’s behaviour deviates from what is typical and requires explanation. However, this format has not yet become universal. The ability to converse with a building emerges only where the digital model is genuinely capable of describing its own behaviour. Otherwise, dialogue inevitably degenerates into a mere retelling of signals and states, rather than an explanation of what is actually happening.
Traditional building operations are built around control. Their primary objective is to observe engineering system parameters and respond to deviations. The logic is straightforward and familiar: acceptable ranges are defined, any excursion beyond them triggers an alarm, and operational staff intervene to correct the issue. This approach can be compared to intensive care. Key indicators that sustain life are monitored, while the observed object itself has no internal representation of its own state. It is effectively unconscious and remains under constant external control. In practice, the most widespread embodiment of this approach is Building Management Systems (BMS). Due to the high cost of implementation and ongoing maintenance, they are typically used to provide high-level control over a limited set of processes - energy consumption, water consumption, the operation of critical building services, and the maintenance of predefined comfort levels. Even when this model is extended through BIM, the situation does not fundamentally change. An anatomical representation and spatial referencing of signals emerge, but an understanding of behaviour does not. The system continues to register individual parameters and events rather than forming a coherent picture of how the building functions over time. Within such a framework, speech is impossible by definition. The system has nothing to communicate beyond sensor readings and the fact that a threshold has been exceeded. Any attempt to implement a “conversation” on top of monitoring inevitably remains a simulation of dialogue - a retelling of signals, not an explanation of meaning. To be able to explain its behaviour, a building must be represented not as an object of control, but as a system possessing an internal understanding of its own processes. Overcoming this methodological limitation is precisely what the live digital twin makes possible.
The purpose of a live digital twin is to form a holistic understanding of how a building operates over time, how its engineering subsystems interact with one another, and how their behaviour changes under the influence of internal and external factors. The key methodological distinction of this approach lies in abandoning the analysis of isolated measurements in favour of analysing process signatures, aimed at identifying anomalies and known behavioural patterns. To establish such a system, a new class of digital solutions is employed, specifically designed to work with building behaviour. These solutions do not function as an extension of BMS; instead, they form an independent digital environment, that is integrated into existing management systems, in which the building gradually acquires an internal representation of its own operation and the dynamics of its engineering systems. The foundation of the digital twin is formed by large volumes of high-frequency data, creating a continuous picture of building behaviour and accumulated within a historical context. Individual measurements are not meaningful in isolation; what carries information is the shape of behaviour - rhythms, fluctuations, recurring operating modes, and the mutual responses of parameters to one another. These processes do not exist independently of the physical object. They are always anchored to the building’s digital anatomy - spaces, zones, engineering circuits, and specific pieces of equipment. This contextual linkage makes it possible to interpret data not abstractly, but in relation to the real asset in operation: it becomes clear not only what is happening, but where it is happening, in which system, and through which interactions with other processes. As data accumulates, the system develops historical memory. The current state is no longer perceived as an isolated snapshot, but as a point along a trajectory of development. This makes it possible to detect slow degradation, distinguish one-off deviations from systemic change, account for seasonal and operational patterns specific to a given building, and form an understanding of its own normal behaviour. At this level, cause-and-effect relationships become discernible. Changes in one subsystem manifest themselves in the behaviour of others, and local interventions begin to be considered in the context of their system-wide consequences. The analytical core of the live digital twin - algorithms for analysis and machine learning - performs the function of a system “brain”: it continuously interprets what is occurring, compares current processes with accumulated experience, detects deviations from normal behaviour, and formulates explainable hypotheses about the causes of observed changes. Unlike the logic of intensive care, where the object remains under external control, the building here acquires an internal mechanism for understanding its own state for the first time. It does not merely react to thresholds and events; it “knows” how it normally operates, recognises when its behaviour begins to change, and is able to explain that difference. In practical terms, this results in the emergence of a language layer between the live digital twin and the human. This layer is implemented using large language models and serves not to interpret raw data, but to translate the system’s already-formed understanding of building behaviour into coherent, explainable responses.
A building begins to speak not because it has acquired a chat interface. It speaks because it has become a system with cognitive capabilities. The live digital twin has formed an understanding of behaviour, and the language interface has made that understanding accessible to humans. Building operations move from a mode of control to a mode of understanding - and from this point on, the rules of the market begin to change.
Yes, the HiPerWare platform can function effectively by using actual operational data and schematic diagrams, eliminating the need for a comprehensive 3D model while still providing meaningful insights.
The HiPerWare platform simplifies ESG (Environmental, Social, and Governance) and CSRD (Corporate Sustainability Reporting Directive) reporting by automatically documenting all operational parameters in a digital circuit. This automation ensures consistent, up-to-date data for sustainability compliance and performance tracking.
By basing decisions on real data rather than human intuition, HiPerWare helps organisations minimise human error and subjectivity. It continuously learns from historical data, identifies recurring patterns, and provides insights that support smarter, evidence-based decisions.
