You describe the part you want in plain English and the AI builds the exact 3D model for you instantly.

Aurorin CAD's flagship ML use case is its LLM-powered chat-to-CAD agent. Engineers type or speak a natural-language description of a mechanical part—including dimensions, constraints, materials, and geometric relationships—and the system's fine-tuned large language model interprets the intent, maps it to parametric operations within the custom B-Rep kernel, and generates a fully editable 3D model in seconds. Unlike wrapper-based approaches that bolt AI onto legacy CAD APIs, Aurorin's kernel was designed from the ground up to accept AI-generated operation sequences natively, eliminating translation overhead and enabling real-time iterative refinement through follow-up conversation. The model understands engineering context (tolerances, manufacturing constraints, standard part libraries) and produces geometry that is not just visually correct but parametrically sound—meaning engineers can modify dimensions and features downstream without rebuilding. This collapses the traditional CAD workflow of sketch → extrude → constrain → iterate into a single conversational loop.

It's like having a master draftsman living inside your computer who draws exactly what you describe before you even finish your coffee.

Multiple AI assistants work on different parts of your design at the same time, like a team of engineers who never miscommunicate.

Aurorin CAD supports multi-agent workflows where several AI agents operate in parallel on different components or sub-assemblies within a single design project. Each agent is responsible for generating or modifying a specific part while respecting shared constraints (mating surfaces, clearance envelopes, load paths, fastener patterns) managed by a central orchestration layer. This approach mirrors how engineering teams divide work on complex assemblies but eliminates the communication overhead and merge conflicts that plague traditional multi-engineer CAD workflows. The orchestration layer uses constraint propagation and conflict resolution algorithms informed by ML models trained on engineering assembly patterns to ensure that changes made by one agent (e.g., resizing a bracket) automatically trigger appropriate updates in dependent agents' work (e.g., adjusting bolt hole positions on an adjacent panel). This is made possible by the AI-native kernel architecture, which exposes a programmatic API that agents can call directly rather than simulating mouse clicks in a GUI.

It's like having a pit crew where every mechanic works on a different part of the car simultaneously and nobody bumps into each other.

The software watches what you're designing and proactively suggests the next feature, warns you about manufacturing problems, and auto-completes repetitive patterns before you ask.

Aurorin CAD's kernel incorporates ML models that continuously analyze the evolving geometry of a part or assembly in real time to provide intelligent design assistance. As an engineer works—whether through chat or traditional UI—the system recognizes geometric patterns (hole arrays, fillet sequences, symmetry planes, standard fastener features) and offers to auto-complete them, dramatically reducing repetitive clicks. Simultaneously, a manufacturability analysis model evaluates the current geometry against learned constraints from manufacturing processes (CNC milling access angles, minimum wall thicknesses for casting, 3D printing overhang limits) and flags potential issues before the design is finalized. This predictive layer is trained on large datasets of engineering parts and manufacturing feedback loops, enabling it to understand not just what the geometry is but what it's for and how it will be made. The tight integration with the custom kernel means these suggestions appear in milliseconds rather than requiring a separate simulation step, keeping the engineer in flow.

It's like autocomplete for your text messages, except instead of finishing your sentences it finishes your engineering designs and tells you if they'll actually work in the real world.