From initial model design to live deployment, AQ provides the verification, validation, and expert guidance that safety-critical organisations need to trust their AI systems.
We provide industry-leading verification and explanation of applied machine learning workflows. Our V&V process covers model assumptions, data quality, performance bounds, and interpretability.
AQ develops best-practice standards and guidance for data-centric engineering applications. We help organisations build internal frameworks that meet regulatory expectations.
Expert consulting in probabilistic modelling and uncertainty quantification. We help engineering teams move beyond point estimates and build models that properly represent what is known about a system.
We deliver bespoke training programmes for academia and industry — including ML for physical systems and digital twins, and probabilistic programming for risk and safety.
WALD is our software tool in development for decision analysis under uncertainty. Built on rigorous probabilistic foundations, it brings structured reasoning to engineering decisions.
End-to-end assurance frameworks tailored to safety-critical deployments. We support clients from initial risk identification through to ongoing monitoring and audit.
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Most engagements begin with a scoping conversation. Tell us about your model, your deployment context, and your assurance concerns.
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