Optimization across the continuous conditions and objective
Traditional engineering optimization can only deliver a solution for a single operating condition, forcing designers to restart the entire process whenever requirements change. Our Multi-Condition Multi-Objective (MCMO) optimization engine, powered by deep Reinforcement Learning (RL), fundamentally breaks this limitation. By learning through exploration—much like an expert engineer develops intuition over years of experience—the system acquires generalizable design principles that transcend individual conditions.
This enables instant generation of optimal designs across continuous ranges of operating conditions, eliminating the need for repeated simulations, redesign cycles, or manual tuning. Companies can rapidly deploy robust, condition-adaptive designs that reduce development cost, accelerate product iteration, and outperform conventionally engineered systems. With MCMO-RL technology, engineering organizations can shift from slow, condition-specific workflows to fully autonomous, scalable, and future-proof design pipelines.
Engineering on autopilot : Next-generation fully automated CAE
Autonomous Engineering: From CAD to Simulation With Zero Human Input
Engineering design still relies on manual CAD parameterization, expert meshing, and painstaking simulation setup. Our autonomous CAE engine removes every manual step. The system interprets geometry, parameterizes CAD features automatically, generates high-quality meshes, and executes multiphysics simulations—fluid, structural, thermal, or coupled—without any human intervention.
Engineering at Scale: Faster Cycles, Lower Cost, Unlimited Exploration
MCMO technology transforms the economics of industrial R&D. Companies can cut design time from weeks to hours, reduce reliance on scarce CAE experts, and explore thousands of design alternatives with near-zero marginal cost. Whether developing pumps, reactors, heat exchangers, vehicles, or consumer products, organizations can innovate at software speed—scaling engineering output without scaling manpower.