Make trustworthy AI models from the start with Saimple
Our static analyzer equips data scientists and AI engineers with powerful features to help them create robust and explainable AI systems in conformity with ISO standards. Trust Saimple to help you build the foundation for reliable AI.
Ensure the safety of critical AI applications with Saimple suite. Trusted by AI engineers worldwide, our software helps you adopt artificial intelligence with confidence in situations where failure is not an option.
Don’t compromise on reliability – use Saimple to build trustworthy AI systems for your most important projects.
Improve your AI design process with Saimple’s step-by-step audit and optimization
Replace empirical validation methodologies with formal methods to ensure the success of your machine learning model performance. Design accurate machine learning models more efficiently and reduce the risk of failure once in production.
Improve the quality of your AI training by using Saimple to help refine your training dataset.
A good training dataset is crucial for the performance of your machine learning systems, but simple statistical checks aren’t enough. Saimple helps you understand how your machine learning system has learned from your dataset, saving you time and effort.
Validate your neural network or SVM with Saimple before moving it into production
Our software provides formal methods to prove the reliability of your AI, helping you make informed decisions about the trade-off between risk and reward in its use. Don’t let the risks of AI adoption hold you back – use Saimple to validate your systems and limit potential risks.
Ensure compliance with ISO/IEC standards and the EU AI-Act with Saimple, the leading SaaS software for AI validation
Our software helps you build trustworthy, regulation-compliant AI systems and manage risk in the face of an international initiative to standardize and establish trust in AI technology.
Validating the reliability of AI algorithms for detecting cardiac arrhythmias in ECGs
Improve AI system design for quality control industry
Measure robustness of data augmentation
