AI governance services that make every model decision auditable, challengeable, and defensible.

AI governance is the set of policies, processes, and technical controls that ensure AI systems behave as intended, can be audited and challenged, and comply with applicable regulations. In practice: documenting how a model was built and what it was trained on, evaluating whether it produces biased outcomes for specific groups, building the infrastructure to explain individual decisions, designing human override paths for automated decisions that affect people, and monitoring the model in production for performance changes that could indicate data drift or unexpected behaviour. Governance is what separates an AI system that can be defended to a regulator from one that cannot.

Governance requirements are highest for AI systems that make or influence consequential decisions about people: credit scoring and lending decisions, insurance underwriting and claims assessment, candidate screening and hiring recommendations, pricing decisions that vary by customer attribute, content moderation, medical diagnosis support, and benefit eligibility determinations. Regulatory requirements vary by jurisdiction and industry, GDPR's automated decision-making rules (Article 22), the EU AI Act's risk tiers, financial services model risk management guidelines (SR 11-7 in the US, PRA SS1/23 in the UK), and sector-specific rules in healthcare and insurance. We help you understand which rules apply to your specific use case before scoping the governance work.

A model card is a standardised document that describes a machine learning model: what it does, what data it was trained on, how it was evaluated, its performance across different population segments, its intended use cases, and its known limitations. Model cards originated at Google and are now a standard component of responsible AI deployment. You need one whenever a model makes decisions that could affect people differently based on protected characteristics, or whenever you need to demonstrate to a regulator, auditor, or customer that your AI system was built and tested responsibly. We produce model cards as a deliverable of the governance engagement, not as documentation produced after the fact.

Bias evaluation covers three stages. First, dataset analysis: examining the training data for representation imbalances across protected attributes (gender, race, age, disability status, geography) that could produce systematically different outcomes for different groups. Second, model evaluation: measuring performance metrics (accuracy, false positive rate, false negative rate) separately for each protected group and identifying where the model performs materially worse for specific segments. Third, outcome analysis: for deployed models, analysing whether actual decisions differ systematically by protected attribute after controlling for legitimate predictive factors. The specific fairness metrics used depend on the use case, equalised odds, demographic parity, and calibration each capture different notions of fairness, and the right metric depends on what discrimination would mean in your context.

Every engagement is scoped and fixed-price. Week 1 produces a written scope and a fixed quote covering the models and regulatory requirements identified, and no work starts without your sign-off. The scope depends on how many models are in play, what documentation you already have, and which regulations apply.

AI explainability means producing a human-readable reason for a specific model decision, for example which two factors drove a loan decline, plus a model-level summary of which features drive predictions. It is technically required under GDPR Article 22 for fully automated decisions with legal or similarly significant effects, under the EU AI Act for high-risk systems, and under financial model-risk guidelines. Practically, it is required whenever a human must review, challenge, or override an AI decision.

Start by inventorying every AI system you use, including the ones embedded in your ERP, ATS, and CRM tools, which most organizations have not identified. Classify each system by risk level, then meet the transparency and AI literacy obligations that already apply. High-risk system obligations carry their own effective dates, so scope against the current published text rather than proposed delays.

Four steps, in order: inventory your AI systems, classify them by risk, meet the transparency and AI literacy obligations, and document a role-based training plan. That is reasonable care over how AI is chosen, used, and monitored: control without bureaucracy. You do not need a Chief AI Officer to start; you need to know what AI you are already running.

Ask for the diagnostic first: a short assessment that finds the AI you did not know you had and surfaces errors you have not identified. Judge the work by its outputs: a model card you can read, a bias evaluation against your data, and an audit trail your team can query without a developer. If governance was added to the pitch rather than the product, those outputs will not exist.

Human-in-the-loop design defines when automated decisions get human review: borderline confidence scores, protected-attribute flags, and high-value cases. The reviewer sees the recommendation, confidence, feature attribution, and case data, then can approve, override, or escalate. Every review decision is logged for the audit trail and for retraining. We design and build the review queue interface, the case presentation, and the override logging, not just the policy document.