Validated in collaboration with leading research and industry organizations.
Get more from the models you already have.
You don't need to change your model stack to see the value. Halley works on trees, neural networks, ensembles – and anything else in your production pipeline today.
Teams discover structure they didn't know was there: interactions that validate domain intuition, or learned relationships that surface new risk factors.
Better understanding of models you already trust is the fastest path to better decisions.
The explanations your validators actually need.
SHAP values tell you that age mattered. They don't tell you that the model learned a nonlinear interaction between income and default that flips direction above a specific threshold.
That's the kind of structure your validators need to see, and the kind your current tools can't surface.
Unlock models you've been forced to leave on the shelf.
Your data science teams already know that more expressive models would improve pricing, underwriting, and fraud detection. The barrier isn't capability – it's explainability.
Current tools produce feature importance scores that satisfy a checklist but don't give model risk teams what they actually need: a clear view of how variables interact, where thresholds kick in, and what the model is really doing at the edges.
Halley removes that barrier. Now you can field state of the art predictive models, and explain what they're doing.
Compliance
Built for regulated industries.
Halley was designed for environments where every model decision needs to be justified, documented, and defended.
Explanations help you understand the patterns your model has learned – and identify exactly where the data disagrees. Outputs are structured for model documentation, audit trails, and regulatory review.
The regulatory direction is clear. The interagency guidance on model risk management (SR 26-2, April 2026) in the US and the PRA's SS1/23 in the UK both emphasize principles-based, risk-proportionate model governance.
This direction is consistent across jurisdictions: regulators are moving away from checkbox compliance and toward meaningful evidence that you understand what your models are doing. Feature importance barely satisfies the old standard. Halley was built for the new one.
Whether your framework is SR 26-2, SS1/23, or an internal model governance standard, Halley gives validation teams evidence they can work with, not artifacts they have to work around.
Deployment
Designed to fit your environment.
Deploys on-premise or in your private cloud. Your data never leaves your infrastructure.
Works with any model that takes structured data as input, from random forests to deep neural networks.
Integrates with your existing model development and validation workflows. No proprietary model formats or vendor lock-in.
Get in touch
See it on your own models.
