.jpg)
Computer-aided design (CAD) in the industrial sector has evolved significantly, but it still faces a central challenge: translating ambiguous or high-level human instructions into precise, editable parametric models. Traditional workflows require engineers to possess deep technical knowledge to operate tools like CATIA, and any change in requirements can involve long hours of remodeling. In this context, artificial intelligence and AI agents are beginning to revolutionize the way designs are created and managed, offering a layer of intelligent automation that not only accelerates processes but also captures and reuses expert knowledge accumulated within organizations.
One of the most promising approaches involves systems that act as cognitive assistants, capable of interpreting vague textual descriptions or generic design intentions and converting them into executable CAD operations. To achieve this, an intermediate representation is required that functions as a bridge between natural language and parametric geometry. This representation must encode not only the parameters and order of operations but also the dependencies between entities, verification rules, and links to specific CAD software tools. This concept, known in the literature as CAD Intermediate Representation (CAD-IR), allows an artificial intelligence agent to retrieve skills derived from expert procedures—for example, recordings of CATIA sessions or engineering notes—instantiate them, and adapt them to new requirements, generating B-Rep models ready for production.
Expert knowledge distillation is key: instead of training models from scratch with generic data, these systems leverage the practical knowledge already available in companies, such as macros, operation logs, or technical descriptions. Thus, an engineer can write an instruction like 'create a mounting bracket with four M8 threaded holes on a rectangular base 200 mm long' and the agent, after consulting its skill base, generates the complete and editable parametric model. This capability drastically reduces design time and minimizes errors, especially in complex components in the automotive or aerospace sector, where precision is critical.
For companies looking to adopt these technologies, having a technology partner that understands both the industrial domain and the latest trends in artificial intelligence is essential. At Q2BSTUDIO, as a software and technology development company, we offer AI solutions for businesses that allow integrating intelligent agents into design, manufacturing, and maintenance workflows. Our team develops custom applications that adapt to the specific processes of each organization, ensuring that artificial intelligence is not a black box, but a transparent tool aligned with business objectives.
Furthermore, implementing these systems often requires a robust and secure cloud infrastructure. We work with AWS and Azure cloud services to deploy AI agent models, manage large volumes of design data, and ensure cybersecurity in sensitive industrial environments. Cybersecurity is a pillar in any digital transformation project, especially when handling intellectual property of critical designs. Likewise, business intelligence plays an increasingly important role: through business intelligence services and tools like Power BI, companies can monitor the efficiency of their design processes, identify bottlenecks, and make data-driven decisions. Process automation, combined with AI agents, allows closing the loop from design intent to manufacturing, reducing delivery times and operational costs.
In summary, the convergence of parametric CAD and artificial intelligence opens a new era for product engineering. AI agents trained with expert knowledge not only better interpret human intentions but also democratize access to complex tools, allowing multidisciplinary teams to collaborate more fluidly. At Q2BSTUDIO, we are committed to bringing these capabilities to industrial companies, offering custom software, cloud integration, and AI solutions that truly make a difference in competitiveness and innovation.
