See what your models have really learned.

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.

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. Halley extracts the full decision logic: the functional forms, the interactions, the conditional relationships.

Your data science teams already know that more expressive models would improve pricing, underwriting, and fraud detection. The barrier has never been capability – it's been 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 your best models and explain what they're doing.

Halley was designed from the ground up for environments where every model decision needs to be justified, documented, and defended. Explanations are complete and deterministic, not sampled approximations. 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. The direction is consistent across jurisdictions: regulators are moving away from checkbox compliance and toward meaningful evidence that you understand what your models are doing. Attribution scores were built for 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.

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 logistic regressions to deep neural networks. Integrates with your existing model development and validation workflows. No proprietary model formats, no vendor lock-in.