Our Technology

We protect your AI in several ways.

Robustness Assessment

Accuracy metrics fail to predict performance on long-tail real-world edge cases, both naturally occurring and adversarial. Empirically score and track model robustness for a more complete picture of model performance.

Identify Failure Bias

Identify at-risk model classes that are the most likely to fail and to which classes such failures are most likely to fail towards, or as we like to call it, 'failure bias', allowing reduction and shaping risk away from key classes.

Adversarial Training

Identify noisy labels along with the most efficient adversarial samples that can be included in your training process to improve both accuracy and robustness metrics for better, more secure AI.

Data Poisoning

Supply chains, insider threats and data breaches put data at risk. Audit your training data with our second-generation approach to identify embedded Trojan attacks, both known and unknown.

AI Firewall

Protect deployed models from model evasion attacks both in batch and real-time.

Responsible AI

Robustness and Security is necessary to achieve Responsible AI and it starts with understanding robustness. Deploy models with confidence, protect your brand and the global pace of AI innovation.

Sectors at Risk

The truth is, if your industry sector uses AI then you are at some risk of adversarial attack. Such attacks are limited only by the creativity and resourcefulness of malicious actors. While we cannot predict all possible attack vectors, our team actively monitors the threat landscape for emerging risks and are committed to making it significantly more difficult for attackers to succeed.