About
We’re building an AI-native engineering platform that fundamentally changes how complex physical systems are designed — and over time, evolves into a new class of foundation model for engineering itself.
01The shift
An engineer describes a system in natural language — a propulsion assembly, a structural bracket under load, a thermal management loop. The platform translates that intent into structured engineering artifacts, then orchestrates the workflow: generate geometry, apply constraints, run simulations, evaluate performance, and iterate.
Engineering is fundamentally reasoning under constraints — balancing strength against weight, performance against cost, efficiency against manufacturability. Most AI systems operate at the surface level of engineering. We aim to model the underlying structure and physics.
Rather than replacing existing engineering software, JusCAD integrates with CAD, simulation, and analysis workflows as an intelligence layer that understands both engineering language and physical behavior.
02How we’re different
Traditional engineering tools execute commands but do not understand intent. JusCAD acts as a domain-aware orchestration layer that converts natural-language objectives into structured engineering workflows across CAD, simulation, optimization, and validation systems.
Most generative design systems produce geometrically plausible outputs without true physical understanding. We represent geometry, constraints, materials, simulation states, and governing equations within a unified latent representation — enabling the system to reason about engineering behavior, not merely generate shapes.
Complex engineering tasks are decomposed into coordinated sub-problems handled by specialized agents for geometry generation, simulation, optimization, verification, and manufacturability analysis. A central orchestrator maintains cross-stage context and iteratively converges toward designs that are both physically valid and operationally useful.
Mechanical, aerospace, electronics, robotics, thermal systems, and beyond — our goal is to build a shared intelligence layer across engineering disciplines. If frontier AI models are becoming the productivity layer for knowledge work, JusCAD aims to become the intelligence layer for engineering workflows — accelerating design cycles from weeks to hours.
03Founders
CEO, Co-founder
Researching on ai native cad archtectural black boxes like cad tokenizer, multimodal cad transformers and physics-aware latent space at the core of JusCAD. Background in Aerospace Engineering and applied ML for engineering systems.
CTO, Co-founder
Co-founding JusCAD with a focus on developing the physics-aware latent space, manufacturing and synthesis capabilities. Background in Computational Mechanics, Neural Networks and Machine Learning for engineering applications.
04Long-term vision
Frontier models excel at language and code, but lack a deep understanding of space. Engineering is, at its core, a spatial discipline — wings, chips, heat exchangers, robots — geometry, topology, and constraints interacting in three dimensions.
LSEM unifies geometry, physics, and constraints in a single latent space. It enables reasoning across structure and physics simultaneously — not just generating designs, but iteratively refining them while explaining its reasoning. Engineering cognition at scale.
05Mission
In the near term, we accelerate engineers. In the long term, we redefine engineering itself — the intelligence layer for the machines, systems, and infrastructure that define the real world.