There are concepts that, for years, reside in the realm of theory and technology conferences, and then suddenly appear in everyday industry conversations. The digital twin is one of them. Five years ago, it was a term used by researchers and a few cutting-edge technology companies. Today, clients are requesting it, tenders are requiring it, and architecture and engineering firms of all sizes are implementing it.
The problem is that many people talk about digital twins without fully understanding what they are, what they aren’t, and what’s needed to actually implement them. This misunderstanding leads to unrealistic expectations, misdirected investments, and projects that promise a lot but deliver little.
At Aufiero Informática we work at the intersection of BIM and emerging technology, and from that experience we want to give a clear, practical and straightforward explanation of what a digital twin in construction is, why BIM is its fundamental basis and how companies in the sector can start to travel that path today.
What is a digital twin and what is not
A digital twin is a virtual replica of a physical object, process, or system that is updated in real time using real-world data. It is not a static 3D model. It is not a photorealistic rendering. It is not a finished BIM model that is archived when the project is complete.
It is a living, connected model that constantly receives information from sensors, management systems, IoT devices and other data sources, and allows monitoring, simulation and decision-making about the physical object without the need to intervene directly on it.
The concept originated in the aerospace and manufacturing industries, where it is used to monitor aircraft engines, industrial turbines, and production lines. But in recent years, it has migrated significantly to the construction and real estate sector, driven by the maturity of BIM, the proliferation of IoT sensors, and cloud processing capabilities.
A digital twin of a building, for example, allows you to see in real time the energy consumption of each floor, detect anomalies in air conditioning systems before they become failures, simulate the impact of a renovation before carrying it out, or manage the predictive maintenance of equipment with real usage data.
The difference between a BIM model and a digital twin
This is the most common misconception, and it’s worth resolving it accurately.
A BIM model is the intelligent digital representation of a building. It contains geometry, data, relationships between elements, and, at higher maturity levels, information on costs, schedules, maintenance, and lifecycle. It is the most comprehensive information base available for a construction project.
But a BIM model, by itself, is static. It reflects the state of the building at the time it was modeled, or at best, at the time it was last updated. It doesn’t change when the real building changes. It doesn’t know if the HVAC system is running at full capacity or if there’s a leak on the fifth floor.
A digital twin takes that BIM model as a starting point and connects it to the physical world through real-time data. The model ceases to be a snapshot of the building and becomes a dynamic mirror reflecting what is happening right now.
In other words: BIM is the anatomy of the building. The digital twin is the living, breathing building, with all its systems functioning and generating information.
Why BIM is the essential foundation of the digital twin
A robust digital twin cannot be built without a high-quality BIM model. This statement has concrete implications that many companies discover too late.
The digital twin needs an organized, coherent data structure linked to the building’s physical elements. It needs to know what each element is, where it is located, how it relates to the rest, and what properties it has. This structure is precisely what a good BIM model provides.
Without BIM, the digital twin has to build that structure from scratch, which is enormously expensive, slow, and prone to errors. With BIM, half the work is already done from the design phase.
But there’s an important condition: not just any BIM model will do. The level of detail and information in the model, what in BIM methodology is called LOD ( Level of Development ), directly determines how rich and useful the resulting digital twin can be. A BIM model with geometry but without property data, specifications, or systems is not a good basis for a digital twin.
That’s why at Aufiero Informática we place so much emphasis on the quality of the BIM model from the earliest stages of the project. What is well-defined in design and construction determines what is possible in operation.
The phases of the digital twin in a construction project
The digital twin doesn’t appear all at once when the project is finished. It’s built throughout the entire project lifecycle, in layers that are added over time.
Design Phase: The project’s BIM model is the seed of the digital twin. In this stage, the data structure is defined, information standards are established, and decisions are made that will impact the digital twin’s usefulness for decades. The richer the information incorporated into the design model, the more powerful the operational digital twin will be.
Construction Phase: During construction, the model is updated with the actual changes that always occur between the planned and the finished product. The as-built model, which reflects the final construction, is the starting point for the operational digital twin. Without an accurate and up-to-date as-built model, the digital twin is based on incorrect information.
Operational Phase: This is where the digital twin unleashes its full potential. The sensors installed in the building begin sending data to the model: temperature, electricity consumption, occupancy, equipment status, and air quality. The model updates in real time, allowing building managers to make decisions based on real data, not assumptions.
Maintenance and evolution phase Over time, the digital twin accumulates historical data that allows identifying patterns, predicting failures, optimizing energy consumption and planning renovations with accurate information about the real state of each element of the building.
Specific use cases: what a digital twin is used for in practice
The benefits of the digital twin cease to be abstract when you look at the concrete use cases that are already happening in the industry.
Energy Management: The digital twin allows for real-time monitoring of energy consumption in each area of the building, identifying inefficiencies and simulating the impact of changes before implementation. Buildings that have implemented digital twins for energy management report consumption reductions of between 20% and 30%.
Predictive Maintenance: Instead of maintaining equipment according to a fixed schedule or waiting for it to fail, the digital twin allows for anticipating failures based on the actual behavior of the systems. This reduces corrective maintenance costs and extends the lifespan of the equipment.
Space management: In office buildings, hospitals, or universities, the digital twin allows you to understand how spaces are actually used: which areas are always full, which are empty, and how occupancy varies depending on the day and time. This information allows you to optimize space allocation and reduce operating costs.
Emergency simulation: Digital twins allow for the simulation of emergency situations, such as fires or evacuations, and the evaluation of the building’s systems response before they actually occur. This improves contingency plans and can save lives.
Renovations and Refurbishments: Before carrying out a renovation, the digital twin allows for simulation on a model of the building’s current state. This significantly reduces unforeseen problems on site and improves the accuracy of budgets.
Sustainability certifications Digital twin data is a valuable input for obtaining and maintaining certifications such as LEED or BREEAM, which require evidence of the actual performance of the building in operation.
What technologies converge in a digital twin
The digital twin is not a single technology. It is the convergence of several technologies working together.
BIM as a structural basis for building data and information, which we have already developed in depth.
IoT (Internet of Things) as the network of sensors and connected devices that generate data in real time: temperature sensors, energy meters, occupancy detectors, access control systems, air conditioning equipment with connectivity.
Cloud computing is the infrastructure that allows data to be stored, processed, and accessed from anywhere in real time. Without the cloud, the volume of data generated by a connected building would be impossible to manage.
Artificial intelligence and machine learning serve as the analytical layer that transforms data into actionable insights. AI detects patterns, generates alerts, makes predictions, and automates management decisions.
Visualization platforms such as interfaces that allow building managers to interact with the digital twin intuitively, view the status of each system in the 3D model, and easily access relevant information.
The role of Octave BricsCAD and BIM tools in building the digital twin
Modern BIM platforms are evolving to incorporate digital twin functionalities. Octave BricsCAD BIM, one of the tools we use at Aufiero Informática, allows you to create models with levels of detail and information that are directly compatible with the most widely used digital twin platforms in the industry.
Interoperability is key in this ecosystem. Open formats like IFC allow a BIM model created on one platform to be used by digital twin platforms without loss of information. This means that choosing the right BIM tools from the start of a project not only impacts the efficiency of design and construction, but also the quality of the digital twin that can be built into an operational system.
Where to begin: the path to the digital twin
The digital twin is not a destination you reach overnight. It’s a journey you take in stages, and the good news is you can start today, right where you are, without needing to have everything figured out from the beginning.
Step 1: Establish a solid BIM practice. If you’re not yet working with BIM, or are only doing so partially, this is the first step. Without a quality BIM model, a digital twin is impossible. At Aufiero Informática, we support design studios and construction companies in adopting and implementing BIM workflows.
Step 2: Define priority use cases. It doesn’t make sense to implement a complete digital twin right away. The most effective approach is to identify the two or three use cases where the greatest value is found—energy management, equipment maintenance, space management—and start there.
Step 3: Design the data strategy from the beginning. What data will be captured, with which sensors, how frequently, and how will it be linked to the BIM model? This definition must be made before construction begins, not after.
Step 4: Choosing a Digital Twin Platform There is a growing ecosystem of specialized platforms. The choice depends on the type of building, the priority use case, the budget, and compatibility with existing BIM tools.
Step 5: Connecting the model to real-time data. Integrating the BIM model with the building’s data systems is when the digital twin comes to life. It’s the most technical step and requires the most professional support.
The time is now.
The digital twin is no longer a technology of the future. It is a reality that is transforming the way buildings are designed, built, and operated worldwide, and Latin America is no exception.
Companies that start building their BIM capabilities today, with the quality and rigor that the digital twin will require tomorrow, will have a real competitive advantage over those that postpone that process.
At Aufiero Informática, we support you on this journey: from implementing BIM workflows to consulting on emerging technologies applied to the construction sector. If you want to understand how your company can begin this journey, we’re here to talk.
Is your company already working with BIM or exploring digital twins? Tell us what stage you’re at and what challenges you’ve encountered. We’d be happy to share our experience.
What it takes to keep a digital twin alive
The hard part of a digital twin is not building it — it is keeping it true. A twin that stops being updated becomes an expensive 3D picture within months: the asset changes, equipment is replaced, layouts are altered, and the model quietly drifts from reality. Once the operations team stops trusting it, nobody opens it again.
Keeping it alive requires three commitments. Someone owns the twin after handover, not just during construction. Every change to the physical asset has a defined route back into the model. And the data feeds — sensors, maintenance records, building management systems — are treated as production integrations, with monitoring and alerts, rather than as a one-off connection made during the project.
Common mistakes when starting a digital twin project
The most frequent mistake is starting from the technology instead of the question. A digital twin built without a specific operational question to answer — energy consumption, maintenance scheduling, space utilisation, safety simulation — ends up modelling everything at maximum detail and delivering nothing anyone uses.
The second mistake is underestimating data quality in the BIM model that feeds it. If assets are not consistently classified and named in the model, no amount of sensor data will make the digital twin reliable. Fixing classification early costs a fraction of fixing it after the twin is already in operation. Explore Octave BricsCAD distributed by Aufiero Informática and check the official Bricsys site for BIM capabilities.
Frequently asked questions about digital twins in construction
What is the difference between a BIM model and a digital twin?
The BIM model describes what the building is and how it was designed; the twin tracks what the building is doing right now. The decisive difference is the flow of operational data: without sensors, meter readings or building systems feeding the model, you have a very good as-built model but not a twin.
Does it require sensors on everything?
No. It starts with the few indicators that genuinely change a decision: energy consumption, temperature and air quality in critical areas, and the condition of the equipment with the highest maintenance cost. Instrumenting the whole building at once produces a volume of data nobody reads. The practical rule is to instrument where there is a recurring decision to make.
At what project stage is it worth starting?
The cheapest moment is during design, because that is when element naming and the information structure are defined. Starting in operation is possible but requires rebuilding that structure from documentation that is rarely complete — which is exactly why the cost rises.
What does it actually do for whoever operates the building?
Three uses pay for the investment in most cases: predictive maintenance, which anticipates equipment failure instead of reacting to it; simulating a change before executing it, such as altering a floor layout and seeing the effect on air conditioning; and traceability for audit and certification, with a performance history rather than a declaration.
What is the biggest mistake in implementation?
Treating it as a technology project rather than an information project. It fails when nobody defines who keeps the model current after handover, how often, and from which source. An out-of-date digital twin is worse than none, because it produces decisions based on wrong data.
How does Aufiero Informática support this path?
Aufiero Informática distributes BIM, design review and coordination, visualisation and document management solutions across Latin America, with local support and regional commercial terms. The team can help assess what already exists in the design workflow and size licences per discipline and per team.
