Experience a x10 to x100 speed up of your onboarding and simulation workflows by using your existing AI tools with Nullspace
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AI Onboarding Tutor
It is easier than ever before to teach your new engineers to use Nullspace. Due to our powerful Python API, engineers new to EM simulation tools can simply interact with Nullspace through LLMs — having the AI act as their coach, tutor, and guide — allowing new users to cut weeks–months of the standard onboarding time and to start delivering accurate simulation results, quickly and without hallucinations.
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AI-orchestrated workflow
The governing Maxwell's equations are still solved with a traditional physics-based solver: full-wave, validated, 3D Method of Moments with no approximations. From parametric analysis, to complex optimizations, to generative design — AI handles the workflow around the simulation, not the simulation itself.
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Generate accurate surrogate data
Nullspace allows engineers and AI agents alike to run significantly faster full-fidelity simulations than established EM legacy tools. Large amounts of very accurate data for radar cross section analysis or antenna design can be generated very quickly.
Set up simulations in plain English
Describe the analysis you want; AI builds the model, runs it, and post-processes results through the Python API. You review and adjust instead of clicking through the setup.
Capture your team’s expertise
Encode how your experts configure and evaluate problems in Python. Colleagues, pipelines, and AI agents run it the same way every time.
Cover the full design space, not just a few points
Parametric sweeps, optimization, and uncertainty quantification run as ordinary scripted steps, becoming standard steps in your workflow instead of one-off efforts per program.
A full NASA Almond RCS study, run end-to-end from one natural-language prompt in 45 minutes, on a laptop.
The speed, scale, and rigor are properties of the Nullspace EM solver. AI doesn't make the physics faster or more accurate; it removes the busywork around the physics-based simulation
Generate training sets in a fraction of the time
What takes legacy tools months of solver runs, Nullspace produces in a fraction of that — making surrogate modeling practical instead of prohibitive.
Train on trustworthy data
Every sample is a full-wave solve, so the surrogate inherits the accuracy of the solver underneath rather than another tool's approximations.
Cover the design space a surrogate needs
Dense, broad sweeps across geometry and frequency — the volume of data a reliable EM surrogate actually requires.
How much are you leaving on the table?
The studies you skip, the designs you simplify, the deadlines you stretch. What if you didn't have to?