Cutting-edge testing frameworks and research advancing AI quality assurance
Our comprehensive testing framework covers functional correctness, safety constraints, fairness evaluation, and robustness analysis. We combine automated testing with expert human analysis.
Advanced methodologies for identifying vulnerabilities through adversarial testing. We evaluate system resilience against prompt injection, jailbreaks, and adversarial inputs.
Systematic approach to identifying and quantifying bias across demographic groups, protected attributes, and output distributions. Includes fairness metrics and mitigation strategies.
Structured approaches to identifying system vulnerabilities through adversarial simulation. Covers attack techniques, threat modeling, and exploitation scenarios.
Comprehensive methodology for evaluating AI system safety through structured testing protocols.
Metrics and methods for assessing AI system resilience to adversarial attacks.
Novel approaches to identifying and quantifying bias in transformer-based AI systems.
Systematic methodologies for simulating adversarial attacks and identifying vulnerabilities.
Real-time detection of performance degradation and behavioral anomalies in deployed systems.
Framework for implementing CASA and ISO standards in AI certification programs.