How it works
A straightforward pipeline, built to fit into workflows engineers already use.
Define the design space
Import your geometry and parameters. Specify the ranges and constraints you actually care about — no reformatting your existing models.
Train fast surrogates
We run a small set of high-fidelity simulations on GPUs and train physics-informed surrogate models that predict outcomes at a fraction of the cost.
Search intelligently
Quantum-inspired optimization algorithms sweep the design space using the surrogates, flagging the most promising candidates for verification.
Verify and decide
Top candidates get full-fidelity verification runs. You get a ranked shortlist with the data behind every recommendation.
Why the pipeline is shaped this way
High-fidelity solvers are trustworthy but expensive; surrogate models are cheap but approximate. The pipeline uses each where it is strongest: solvers anchor the training data and verify the finalists, surrogates carry the wide search in between.
That means you never make a final decision on a surrogate's word alone — every shortlisted candidate is re-run at full fidelity before it reaches your design review.
The optimization layer uses quantum-inspired methods — classical algorithms that borrow search strategies from quantum annealing — because they handle the discrete, constraint-heavy landscapes typical of engineering trade studies well.
Want the deeper technical picture? See our technology stack or book a technical call.