Engineering and regulatory teams make high-stakes decisions every day — which simulations to run, which experiments to skip, whether a submission is ready. SigmaTwin gives them the confidence to act, backed by quantified trust.
A simulation campaign costs $500K. A regulatory rejection costs $2M and 18 months. In both cases, the underlying question is the same: do we have enough evidence to decide?
Today, that question gets answered by gut feeling, over-engineering, or “run 200 more just to be safe.” SigmaTwin replaces guesswork with quantified trust — scoring every model prediction, every submission gap, every stop/go decision with traceable evidence.
We don’t replace your tools. We make them smarter. Your solver stays. Your regulatory process stays. SigmaTwin adds the intelligence layer that tells you when you know enough to act.
Tells you what to simulate next, when to stop, and where your model is trustworthy — without replacing ANSYS, STAR-CCM+, or OpenFOAM.
For: Simulation engineers, CAE managers, and VP Engineering at pharma, medtech, automotive, energy, and aerospace companies running CFD, FEA, thermal, or DEM simulations.
Screens your regulatory submissions against 400+ deficiency themes extracted from real enforcement actions — before you file.
For: Regulatory affairs, CMC leads, and quality heads at pharma, biotech, and medical device companies preparing FDA, EMA, PMDA, or MHRA submissions.
SigmaTwin was founded on a simple observation: high-stakes engineering decisions require quantified confidence, not just answers.
A simulation surrogate that says “the stress is 247 MPa” is useless if you don’t know whether that prediction is reliable for this specific design point. A screening tool that flags “your CMC package has gaps” is useless without specific regulatory citations, risk scores, and traceable evidence.
Both problems have the same root cause: tools that give answers without telling you how much to trust them.
SigmaTwin builds decision tools that score their own confidence — whether that’s a machine learning surrogate proving its accuracy on your simulation data, or a rule-based screening engine tracing every gap back to a real enforcement action. If the evidence isn’t strong enough, we say so. That’s the standard engineering and regulatory teams deserve.
Every prediction scored. Every recommendation cited. Every tool honest about what it knows and what it doesn’t.
From simulation campaigns to regulatory submissions to manufacturing quality — wherever high-stakes decisions depend on evidence, SigmaTwin provides the confidence to act.
SigmaTwin was born from direct experience with the gap between what engineering tools produce and what teams actually need to make decisions. Simulation solvers give results without stopping criteria. Regulatory databases show what happened to others without screening your own documents. Both leave the hardest question unanswered: do we have enough evidence to decide? SigmaTwin exists to answer that question — with scored confidence, not guesswork.
Send us your simulation results or your draft submission. We’ll show you what we find — with your actual data, not a canned demo.