Xtrable
Artificial intelligence is being deployed into environments where decisions affect lives, livelihoods, and public trust. Yet most AI systems operate without adequate oversight. Models are opaque. Bias goes undetected. Accountability is unclear.
Xtrable is building expertise and tooling to address this gap. Our focus is on practical, auditable AI governance that works for any organisation deploying AI where transparency and accountability matter.
The governance gap
Organisations deploying AI face a growing regulatory landscape, from the EU AI Act to sector-specific requirements in finance, healthcare, and government. Most lack the tooling and frameworks to demonstrate compliance, detect bias, or explain automated decisions to the people affected by them.
Areas of Focus
Our AI governance work spans three interconnected disciplines.
Bias Detection
Identifying and measuring bias in AI models across protected characteristics. Systematic testing against fairness criteria before and after deployment.
Model Governance
Frameworks for tracking model lineage, training data provenance, performance drift, and decision accountability throughout the model lifecycle.
Regulatory Compliance
Mapping AI deployments to regulatory requirements including the EU AI Act, UK AI regulation, and sector-specific obligations in finance, healthcare, and public services.
Our Approach to Responsible AI
We take a practical, engineering-led approach to AI governance. Our work is grounded in the belief that responsible AI is not a compliance exercise but an engineering discipline.
- Explainability. AI systems must be able to explain their decisions in terms that affected parties can understand. This is not optional in high-trust environments; it is a fundamental requirement.
- Auditability. Every model decision should be traceable to its training data, configuration, and deployment context. Audit trails must be comprehensive and tamper-evident.
- Fairness testing. Bias detection must be systematic, not ad hoc. Models should be tested against defined fairness criteria across all protected characteristics before deployment and monitored continuously thereafter.
- Human oversight. Automated decisions that affect people must include appropriate human review mechanisms. The level of oversight should match the severity and reversibility of the decision.
Sector Relevance
AI governance requirements vary significantly by sector. Our expertise covers the environments where the stakes are highest.
| Sector | Key Concerns | Regulatory Context |
|---|---|---|
| Government and Public Services | Algorithmic decision-making affecting citizens, welfare, immigration, policing | UK public sector AI guidelines, Equality Act 2010, EU AI Act (high-risk category) |
| Financial Services | Credit scoring, fraud detection, anti-money laundering, insurance pricing | FCA guidance on AI, PRA expectations, Consumer Duty obligations |
| Healthcare | Clinical decision support, triage, resource allocation, diagnostic AI | MHRA requirements, NHS AI governance, patient safety obligations |
| Defence and Security | Threat assessment, surveillance, autonomous systems | Defence AI strategy, NATO AI principles, security classification constraints |
Advisory and consulting
We offer consulting services to help organisations assess their AI governance maturity, identify risks, and build practical frameworks for responsible AI deployment. Our approach is hands-on and grounded in real-world implementation experience.
Looking Ahead
Xtrable is actively developing tooling to support AI governance at scale. Our goal is to bring the same model-first, auditable approach that underpins NeoArc Studio to the challenge of AI oversight, creating systems where every model, every decision, and every data dependency is visible, traceable, and accountable.