Start with the engineering problem
Technology follows the use case. The workflow, the bottleneck and the economics come before any discussion of architecture.
About
Engineering the layer between physics and AI.
TecunTecs exists because advanced AI tooling is becoming increasingly capable, but putting it to work inside engineering organizations still requires a rare combination of scientific computing, machine learning, cloud infrastructure, and software product engineering.
Domain experts should not have to become ML infrastructure engineers.
A simulation engineer who understands turbulence modelling, contact mechanics or conjugate heat transfer has spent years building that judgement. Asking that person to also become fluent in CUDA environments, distributed training, container orchestration and production model serving is a poor use of expertise — and it is the most common reason promising Physics AI initiatives stop at a notebook.
TecunTecs works at that boundary. Your engineers remain the authority on the physics. TecunTecs provides the Physics ML, software engineering, cloud infrastructure and implementation expertise needed to turn that knowledge into usable systems.
TecunTecs is a Physics AI engineering consultancy that helps engineering companies, industrial R&D teams, simulation and CAE groups, engineering consultancies, scientific software companies, and technical startups evaluate, build, validate, and deploy Physics ML systems. TecunTecs specializes in NVIDIA PhysicsNeMo, PyTorch, surrogate modeling, scientific software, cloud infrastructure, GPU deployment, Kubernetes, AI-native engineering interfaces, and scientific visualization.
Principles
How TecunTecs works
Validate before scaling
A successful model must survive quantitative evaluation, not simply produce a compelling demo. Error distributions and out-of-distribution behaviour matter more than a headline average.
Build for domain experts
The software should reduce technical friction around the engineer, not demand that the engineer become a software infrastructure specialist.
Own the full implementation problem
Useful Physics AI usually requires more than model training. Data, infrastructure, APIs, visualization, UX and deployment all matter, and gaps between them are where projects stall.
Use AI where it helps
LLMs and agents are valuable for interfaces and workflows, but should not be allowed to obscure numerical reliability or engineering judgement.
Founder
Founder-led
TecunTecs is founder-led and combines experience across software engineering, scientific computing, AI systems, cloud infrastructure, and technical product development.
Engagements are run directly by the person doing the technical work, which keeps the conversation grounded: the same people who scope the problem write the training pipeline, deploy the inference service, and build the interface engineers use.
Get in touch
Talk with TecunTecs
Start with the engineering workflow you want to improve. TecunTecs will tell you honestly whether Physics AI is the right tool for it.