LLM BIM: Querying IFC models with natural language
The LLM BIM makes accessing data from IFC models simpler, faster, and more intuitive thanks to natural language. Discover how it works through a concrete use case.
The construction industry is witnessing the emergence of a technology destined to change the way we interact with informational models: the Large Language Models (LLM). While in recent years BIM has allowed for the collection and organization of an ever-increasing amount of data within digital models, today the challenge is no longer just to produce quality information, but to make it easily accessible and usable.
Until now, querying an IFC model meant knowing how to navigate between classes, Property Sets, using filters, schedules, or dedicated queries. These activities require specific skills and, especially in more complex models, can be time-consuming and limit access to information to only expert users.
The evolution of Large Language Models (LLM) is opening a new scenario. Thanks to artificial intelligence, the BIM model can become queryable through natural language, allowing users to obtain information, analyze data, and identify elements simply by asking a question, just as happens in a normal conversation.
The value of LLM BIM lies precisely in this paradigm shift: it is no longer the user who must adapt to the structure of the model, but the system that interprets requests made in natural language and identifies relevant information. Access to data thus becomes more immediate, allowing for a simpler and more effective consultation and utilization of the informational heritage of the model.
This evolution is set to play an even more significant role with the growth of Digital Twins and platforms that integrate BIM, GIS, and openBIM. When managing thousands of models, millions of IFC objects, and billions of properties, the ability to interact directly with the data no longer represents just an innovative feature but a new way to access knowledge.
This is not a futuristic scenario. The most advanced Geospatial Digital Twin platforms are already experimenting with this convergence between openBIM, GIS, and Large Language Models. But what does it mean, in practice, to interact with an IFC model? In the following paragraphs, we will see how artificial intelligence can transform natural language into a tool for querying BIM data, through a concrete usage example.
Contents
What is LLM BIM and how it changes querying IFC models
The LLM BIM is the application of Large Language Models (LLM) – artificial intelligence models capable of understanding and generating natural language – to the world of Building Information Modeling (BIM). In practice, it allows querying an informational model using natural language, without having to resort to specific procedures.
It is often associated with the idea of a simple chatbot capable of answering user questions. In reality, when we talk about LLM BIM, the true innovation lies not in the conversation itself, but in the ability to access the informational heritage contained within IFC models in a simple and immediate way.
In a traditional workflow, obtaining information from an IFC model requires knowing where the data is stored and using specific tools like filters, schedules, queries, or property tables. For example, if you want to identify the room with the largest area, know the use of the spaces, or check the features of a particular element, it is necessary to manually explore the model and interpret its informational structure.
With an approach based on LLM BIM, the paradigm completely changes. The user no longer interacts with the model through menus, windows, and commands, but through a conversation. It is sufficient to formulate a question in natural language — for example, “What data does the model contain?” or “What is the room with the largest area?” — for the system to interpret the request, pinpoint the relevant information within the IFC model, and provide a comprehensible answer.
This does not mean that artificial intelligence replaces BIM or IFC. On the contrary, the informational model continues to represent the authoritative source of data, while the Large Language Model becomes a new interface for accessing information. The responses generated do not stem from the “knowledge” of the LLM, but from the properties, relationships, and attributes already present in the model. In other words, the LLM does not add information to the BIM, but makes it much simpler to identify, understand, and use it.
A concrete use case of LLM BIM: querying an IFC model
To understand the potential of LLM BIM, let’s look at a concrete use case. The goal is to query an IFC model using natural language, without resorting to filters, schedules, or manual queries. Through a simple conversation, the system interprets the user’s requests and accesses the information contained in the model, providing precise answers, analyses, and data representations. The following examples show some of the possible applications of this new approach.
Exploring the contents of the model using natural language
One of the first obstacles when opening an IFC model is to understand what information it contains and how it is organized. Especially in complex projects or those developed by other professionals, identifying the available elements and their properties can take time and require a good understanding of the model’s structure.
With an approach based on LLM BIM, this preliminary phase becomes much more immediate. It is sufficient to ask, for example, “What data is available in the model?” for the system to analyze the content of the IFC and return an overview of the information present. In this way, the user can quickly understand which categories of objects are included in the model, which properties are available, and what queries can be executed.
This initial analysis represents the starting point for all subsequent queries, allowing for navigation within the model without having to manually explore the data structure.
BIM viewer with 3D IFC model and chat panel: a request in natural language returns an overview of the information available in the model
Identifying the Use Destinations of Spaces
After identifying the available data in the model, more specific requests can be formulated. For example, by asking “What are the possible use destinations of the spaces?”, the LLM BIM analyzes the information associated with the environments and returns the list of use destinations present in the model.
The analysis occurs directly on the model’s data, without the need to pre-identify properties or create dedicated filters. The user interacts solely through natural language, while the system interprets the request and identifies the relevant information.
BIM viewer with 3D IFC model and chat panel: in response to a query about use destinations, the system analyzes the IFC model and returns the different types of spaces present
From Information to Data Visualizations
The LLM BIM is not limited to retrieving information contained in the IFC model, but can also organize and present it in a more effective form for analysis. For example, after identifying the purposes of the spaces, one can ask the system to represent them through a chart that highlights their distribution within the model.
This capability allows for a quick transition from raw data to an easily interpretable representation, supporting activities of analysis and verification of information without resorting to manual processing.
IFC model accompanied by a chart that visualizes the distribution of the identified purposes of the spaces in response to a request made in the chat
Identifying and Selecting Model Elements
Natural language querying is not limited to consulting information but can also be used to identify and select specific elements of the IFC model. For example, a request like “Select all bathrooms” or “Which bathroom has the largest area?” allows the system to automatically identify the spaces that meet the specified criteria.
The user does not need to know the classifications used in the model nor set filters or search criteria. It is the LLM that interprets the request, identifies the corresponding objects, and returns the result by highlighting them directly in the model or describing them with characteristics.
This capability makes exploring the model more intuitive and faster, allowing for a transition from information search to direct interaction with BIM objects.
BIM Viewer with bathrooms highlighted in the model in response to a request made in natural language in the chat panel
Why IFC is Fundamental for LLMs
Today, if it is possible to query a BIM model using natural language, it is not only due to Large Language Models. The real difference comes from the quality of the information model and, above all, the way information is organized. An LLM is capable of understanding a request expressed in natural language but does not possess intrinsic knowledge of the content of a BIM model. To provide a correct answer, it must access structured, coherent, and semantically defined data. This is precisely the function of Industry Foundation Classes (IFC), the open standard at the basis of the openBIM approach.
The IFC standard does not merely describe the geometry of a building. It defines a common language to represent objects, properties, and relationships, allowing for the unique identification of spaces, walls, doors, systems, and any other element of the model.
When a user asks a question like “How many bathrooms are there?” or “What is the room with the largest area?”, the LLM does not invent the answer or deduce it from context. It interprets the request and translates it into a query of the information present in the IFC model. The quality of the answer thus depends on the quality of the available data and their proper organization. This is where the value of openBIM emerges: using open standards like IFC means having reliable, interoperable, and time-accessible data, which creates the ideal conditions for Large Language Models to query and optimize the informational wealth of the project. Without a model structured according to these principles, even the most advanced artificial intelligence system would lack reliable data to base its responses on.
Beyond LLM BIM: When openBIM, GIS, and AI Work Together
Querying an IFC model in natural language is only the first step. The true potential of artificial intelligence emerges when the information model becomes part of a broader digital ecosystem, where openBIM data, geospatial information, and other information sources can communicate with each other.
In this scenario, the IFC continues to represent the authoritative source of information about the work, describing its geometry, properties, relationships, and technical characteristics according to an open and interoperable standard. GIS, on the other hand, adds the territorial context, allowing buildings and infrastructure to be placed in their real environment and related to cartography, technological networks, environmental data, IoT sensors, and many other information sources.
The Large Language Model becomes the access point to this wealth of data. Through natural language, the user can query not only the IFC model but a collection of information from different systems, without needing to worry about where it is stored or which application manages it.
It is this convergence of openBIM, GIS, and artificial intelligence that constitutes one of the key elements of Geospatial Digital Twin. In an integrated digital environment, the IFC model is no longer a file to consult but a component of a dynamic information system, capable of interacting with the territory and with the data describing the entire lifecycle of the work.
From this perspective, the LLM is not limited to simplifying the querying of an IFC model but makes access to information contained in the openBIM ecosystem more immediate. The ability to use natural language allows a greater number of users to consult, understand, and utilize the data from the model, improving collaboration across different disciplines and increasing the value of information over time.
If you want to concretely experiment with this approach and understand how to integrate IFC models, GIS data, and artificial intelligence into a single digital environment, you can access a Geospatial Digital Twin platform for free and discover how to enhance the informational heritage of your projects through openBIM technologies.
Frequently Asked Questions (FAQ)
What is an LLM BIM?
An LLM BIM is the application of Large Language Models to Building Information Modeling. It allows querying an IFC model using natural language, making it easier to access the information contained in the model.
Is it possible to query an IFC model with natural language?
Yes. Thanks to Large Language Models, it is possible to ask questions in natural language, such as “What is the room with the largest area?” or “How many bathrooms are there?”, getting answers based on the data from the IFC model.
Does the LLM replace BIM or the IFC format?
No. The LLM does not replace BIM or the IFC format. The IFC model continues to represent the authoritative source of information, while the Large Language Model interprets user requests and facilitates access to the data.
What advantages does LLM BIM offer?
LLM BIM allows for quickly identifying information, analyzing data, selecting model elements, and creating visualizations without resorting to filters, queries, or complex procedures, improving the accessibility of BIM data.
Why is the IFC format fundamental for LLMs?
LLMs can provide reliable answers only if they query structured and semantically defined data. The IFC standard organizes geometries, properties, and relationships of the model, allowing artificial intelligence to correctly interpret requests and retrieve relevant information.
What is the role of LLM in Geospatial Digital Twins?
In Geospatial Digital Twins, the LLM enables querying using natural language data from IFC models, GIS systems, and other information sources, simplifying access to information within an integrated digital ecosystem.
