SciPhy is an autonomous simulation platform that uses AI agents to automate complex engineering workflows, from design of experiments setup through post-processing. The platform handles computational heavy lifting, enabling engineers to focus on design decisions and insight extraction rather than job scripts. It claims a 1000× speedup in simulation workflows.
Funding
Funding not disclosed
Founders
Product
Problem
Engineering teams spend excessive time on manual simulation workflows, moving between siloed expertise, disconnected tools, and repetitive tasks like DOE setup and post-processing. This fragmented approach slows design iteration, delays critical performance insights, and diverts engineers from higher-value analysis and decision-making.
Solution
SciPhy provides a deeply integrated simulation agent platform that automates complex engineering workflows end-to-end, from design of experiments setup through post-processing. The platform's agents work collaboratively to drive every step of the simulation process, delivering answers rather than job scripts. Engineers interact with the system through a natural-language interface, specifying design goals like "maximize pressure ratio, hold η ≥ 90%," and receiving candidate experiments and results. By handling the computational heavy lifting, SciPhy enables engineers to focus on rapid iteration, design improvements, and extracting real engineering insight from simulation data.
Target Audience
Primary users are simulation and design engineers in industries such as aerospace, automotive, and energy who need to accelerate complex performance analysis and design optimization.
Features
- Autonomous simulation agents that manage DOE setup, execution, and post-processing without manual scripting
- Natural-language goal specification that translates engineering targets into candidate experiment recommendations
- Adaptive experiment selection that suggests optimal test strategies (e.g., trailing-edge, full-factorial, adaptive) based on current results
- Real-time performance tracking with spawn IDs, efficiency metrics, and delta values for each simulation run
- 1000× speedup in simulation workflow efficiency, as measured across 1,240 studies