We benchmark explanation techniques to ensure they match your model's logic and stakeholder needs.
The Recommendation Engine
Different stakeholders need different proofs. We validate your AI model's explainability for technical fidelity, stability, and human utility, ensuring you deploy with total confidence.
We mathematically prove that an explanation faithfully represents the model's true decision logic, catching 'hallucinated' justifications.
Automated LLM personas (Risk Officers, Physicians) stress-test explanations to ensure they are actionable and clear for the target audience.
We test if similar inputs yield consistent explanations. If a minor data perturbation changes the explanation, the model is untrustworthy.
Lucas Atkins
Head of AI Alignment
Explanations must be faithful to the model's decision boundary, not just plausible sounding.
Insights must be intelligible to the specific stakeholders (Regulators vs. Users) who rely on them.
Teams need immutable evidence and scored reports to pass model risk assessments.
Latest Research
How opaque AI decisions in legal workflows increase risk, bias, and accountability gaps.
Why explainability is essential for credit, fraud, and risk models in regulated finance.
The case for transparent medical AI where trust, safety, and clinical decisions are critical.
Combine technical metrics, persona validation, and audit-ready reports in one platform.
No credit card required for Sandbox