How is our AI solution revolutionizing carbon impact calculation?

Assessing the carbon impact of a project is a necessary step to monitor the decarbonization trajectory of the building sector. However, this process can be time-consuming, particularly because it relies on often heterogeneous data: lists of equipment and materials presented in various forms (technical nomenclatures, commercial references, abbreviations, etc.). This diversity makes manual processing lengthy and prone to errors.

At Nooco, our AI offers a solution by automating part of this process:

• Analyzing and understanding our clients’ input data
• Standardizing the data into a format accepted by Nooco
• Linking the standardized data to items in our library (connected to European carbon databases)

What’s the benefit of this process? A faster carbon impact assessment for construction projects, while eliminating many human errors.

Why the boom of Large Language Models is transforming AI development?

Language models have always played a central role in the development of artificial intelligence (AI), especially for tasks involving text understanding and generation. Before the emergence of large language models (LLMs), research was dominated by more modest approaches, such as Language Model Prototypes (LMP) or algorithms based on Word2Vec and its derivatives. These approaches used relatively lightweight models to learn semantic relationships between words, but their ability to grasp the subtleties of language remained limited.

So, what sets LLMs apart from their predecessors? Unlike LMPs, which often rely on targeted datasets and simple architectures, LLMs leverage enormous volumes of textual data and complex neural network architectures like the Transformer, which has revolutionized the field since 2017. This technological leap has enabled LLMs not only to understand words but also their context, complex relationships, and even cultural and idiomatic nuances.

The rise of LLMs has revolutionized many industries, and the field of environmental analysis is no exception. For our import engine, they bring three major benefits:

• Increased accuracy: LLMs understand a wide range of technical terms and can map them to standardized databases, even when input data is poorly structured or imprecise.
• Accelerated processing: LLMs eliminate delays associated with manual processes, delivering results in near real-time, even for complex or large volumes of data.
• Remarkable adaptability: LLMs can adapt to a wide variety of use cases, from buildings to industrial infrastructures, without requiring complex configurations.

Thanks to this advancement, the shift from LMPs to LLMs has transformed our use of AI, making our processes faster, more adaptable, and capable of handling complex use cases that previous approaches like Word2Vec could not efficiently address.

What will our new engine bring?

At Nooco, we are now integrating the capabilities of Large Language Models (LLMs) into our carbon analysis engine. This new tool combines our industry expertise with the power of large-scale language models to handle increasingly complex, diverse, and large volumes of data.

The expected improvements are significant:

• Major time savings: What previously required several days of analysis can now be completed within an hour.
• Enhanced reliability: The results generated require far fewer manual checks thanks to more accurate data understanding and matching.
• More powerful and scalable AI: Continuous improvement of the engine is simplified, ensuring rapid adaptation to the diverse needs of our users.

Thanks to the LLM, the manufacturer reference ‘78340-KUBAIR F400 MV 450’ from VIM is accurately recognized through the model’s ability to understand and process manufacturer references.

Conclusion

At Nooco, AI is not just a tool—it’s a solution that is reinventing how we assess the environmental impact of buildings. With this new LLM-powered engine, we are pushing boundaries to simplify data management, speed up analyses, and deliver unparalleled reliability to our users.

Our teams are actively working on its implementation, and this enhanced version will be available very soon. Stay tuned!