Benefits
Models that fit to your data
Based on your real-world data collection, we create mathematical models tailored for safety evaluation, uncertainty quantification, and SOTIF analysis.
A systematic approach to demonstrable safety evidence
Our workflow combines statistical methods, simulations, Bayesian inference, and structured analysis to deliver reliable evidence for your targeted tolerable risk.
Explainable statistics to understand system limitations
Autonomous systems must anticipate what comes next.
We connect quantitative risk assessment, Bayesian reasoning, and performance limits to give you explainable statistical insights, transparent safety arguments, early detection of potential triggers, and measurable evidence for ISO 21448
You gain clarity where systems face uncertainty.
Startups
Innovation moves fast — but safety cannot be an afterthought.
For AI-driven startups working with incomplete data and rapid prototyping, Bayesian reasoning enables structured, scalable safety foundations that match your pace.
We help you apply Bayesian Networks early in development, quantify uncertainty in concepts and prototypes, create explainable, traceable arguments for ISO 21448 / ISO 26262, and prepare for future OEM collaboration and assessments.
Automotive Suppliers
Suppliers must demonstrate clear, quantifiable safety evidence, even without full-system validation responsibility.
We help you translate system-level safety goals into component-level Bayesian models and structured arguments aligned with ISO 26262 and SOTIF.
You get deliverables that are technically sound, traceable to OEM safety goals, and ready for a seamless integration into higher-level safety cases.
Innovation Units in OEMs
Safety in autonomous systems is no longer just about testing — it’s about understanding system behavior.
We combine Bayesian reasoning, SOTIF insights, and FuSa structure to help you explain complex AI and autonomy behavior, quantify rare or safety-critical events, justify tolerable risk values with data-driven arguments, and meet stringent OEM validation requirements.
With transparent evidence and quantitative reasoning, you can prove what others can only assume.
How to deal with incomplete data
Real-world data is never perfect — but safety decisions can’t wait.
Bayesian Networks combine available data with expert knowledge to infer missing information and uncover hidden dependencies.
The result: transparent, explainable insights, even under uncertainty.
How to face the "unknown"
Autonomous systems will always encounter unexpected situations.
We help you identify, model, and manage these unknowns — turning uncertainty into measurable, explainable safety.
Because true safety means being prepared for what cannot be predicted.
Complex and unexpected scenarios
Safety-critical events are rare — and cannot be fully tested on real roads.
By modeling interactions and predicting rare events, Bayesian Networks help you quantify system behavior beyond what testing alone can show.
This creates confidence where experience falls short.
Why a systematic approach matters
A safety argument is only as strong as its foundations.
By documenting your assumptions, decisions, and data context systematically, we create a traceable, scientifically sound framework for SOTIF and Functional Safety.
