Audits for algorithms and data
Our Algorithm & Data Audit is a comprehensive, hands-on evaluation of your AI systems, designed to surface and address issues related to safety, bias, and alignment. In the research phase of model development, we will catch issues before they become real-world problems. In the monitoring phase, we verify that model and data drift have not created any new problems over time.
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We perform structured analyses of your training data and model behavior to assess for distributional bias, disparate impact, label leakage, and misaligned incentives. This includes both statistical evaluations—such as subgroup fairness metrics and counterfactual testing—as well as process audits focused on how data is collected, annotated, and versioned.
A modern approach for today's AI
In the era of large language models and generative AI, these concerns have become significantly more complex. Unlike traditional models, foundation models are often trained on massive, opaque datasets and deployed in open-ended contexts, making behavior harder to predict and harder to align. These systems can amplify subtle biases, hallucinate false or toxic outputs, and resist conventional interpretability methods.
Our audits are tailored for these modern risks—offering both red-teaming-style stress testing and alignment-focused evaluations to ensure your systems behave reliably, safely, and in line with your values.
Measuring up
Beyond evaluating the model, a well-scoped audit allows you to reduce organizational risk, strengthen regulatory readiness, and increase stakeholder trust. Whether you're preparing for external scrutiny or simply want to get ahead of emerging best practices, a technical audit signals a proactive, responsible approach to AI deployment.
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All audits are conducted by a PhD-trained machine learning expert with in-house experience at leading AI companies, using the same methodologies that power internal safety and fairness reviews at the frontier of the field.