WhiteBox
Explainable AI for critical systems
Transparent intelligence for mission-critical systems.
WhiteBox helps leaders understand, trust, and justify the AI systems they rely on. We specialise in explainability, decision transparency, and making complex models ready for scrutiny.
AI you can stand behind.
Explainability turns black-box models into systems you can question and discuss. It shows which signals drove a decision, how robust that decision is, and what might happen if the world around it changes.
For high-impact environments, this clarity reduces risk, supports safety cases, and makes it easier to defend outcomes with regulators, boards, and customers.
See the reasoning, not just the result.
Understand which inputs mattered most, and how decisions shift under different conditions.
De-risk launches and upgrades.
Spot brittleness, bias, or drift early, when changes are still inexpensive and controlled.
Give oversight real visibility.
Provide boards, regulators, and assurance teams with evidence they can follow and challenge, not just metrics on a slide.
Practical explainability for real systems.
We work with teams who already have models in place—or are about to deploy them—and need a clear, independent view of how those systems behave.
Instead of generic “AI transformation” projects, WhiteBox focuses on a small number of high-value questions:
- What is this model actually doing with the data we feed it?
- Where is it strong, and where is it unexpectedly fragile?
- How do we explain its behaviour to people who are accountable but not technical?
Our work often feeds into safety cases, internal approvals, procurement decisions, and regulatory conversations.
Model and decision reviews
We look at how your models use data, where they are confident, and where they are fragile. You get a clear map of how decisions are made today.
Transparency packs for stakeholders
Plain-language explanations, visuals, and talking points you can use with boards, regulators, and non-technical teams.
Risk and governance support
Practical checks for bias, drift, and failure modes, plus simple routines to keep your AI within agreed risk boundaries.
Clarity without the noise.
WhiteBox brings research-grade methods into a format your organisation can actually use. We focus on the few explanations that matter, not every possible graph.
We start by listening: understanding your system, constraints, and who needs to be convinced.
We analyse the behaviour of your models, not just their metrics, to see how they really make decisions.
We translate technical findings into language and visuals that work for your teams and oversight bodies.
We leave you with concrete next steps—not just a report—so you can act with confidence.
What you walk away with.
Our work is designed to leave you with concrete artefacts and reusable patterns—not just a one-off slide deck.
Auditable decision flows
End-to-end traces from input to outcome, with visuals and language that can go straight into board packs or regulator briefings.
Sharper risk and failure insight
Early visibility of brittleness, bias, drift, and edge cases—before they become incidents or reputational issues.
Faster, safer sign-off
Evidence that lets stakeholders green-light AI systems with more confidence and fewer last-minute objections.
Reusable explainability assets
Templates, reporting structures, and explanation patterns you can reuse across models rather than starting from scratch each time.
See it in practice
Explore research and case studies showing how WhiteBox explains real models in finance and longitudinal data.
Let's talk about your system.
Email is the easiest way to reach WhiteBox. Share a little context about your system, and we'll reply with a concise, technical point of view.
hello@whiteboxai.co.uk
We aim to respond within 1–2 business days.