Safe Intelligence offers deep validation AI systems that rigorously test and improve the reliability of AI models. Their technology helps organizations ensure their AI systems perform as expected and meet required safety standards.
Funding
$5.2M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Machine learning models are increasingly complex, making it difficult to ensure their reliability and safety in production environments. Traditional statistical testing methods often fail to detect hidden fragilities that can lead to critical failures, especially in high-stakes domains. This lack of robust validation creates significant risks for organizations deploying AI in areas like finance, mobility, and robotics.
Solution
Safe Intelligence offers a deep validation platform for AI models, providing correctness guarantees and automatically improving model robustness. Their technology uses formal verification and robustification techniques to identify vulnerabilities that traditional testing might miss. The platform supports a wide range of neural network and decision tree models, enabling users to define safety cases, verify model performance against those specifications, and apply state-of-the-art robustification methods. Continuous analysis and reporting provide insights into model performance over time, ensuring quality control and guiding future development.
Target Audience
The primary target audience includes organizations in safety-critical industries such as finance, aviation, mobility, and robotics, as well as AI developers seeking to improve the reliability and trustworthiness of their models.
Features
- Formal verification methods for correctness guarantees and detection of adversarial examples
- Automated robustification techniques to improve model resilience without additional data
- Support for expressive safety specifications, including robustness, noise patterns, and bias-field considerations
- Compatibility with standard machine learning frameworks such as PyTorch and TensorFlow
- Continuous performance reports and analytics for quality control and development guidance
- CI/CD integration for automated building, verification, and monitoring with alerts
- Options for on-premise or private cloud deployment to avoid IP leakage