We validate, audit and stress-test machine learning systems so your organisation can deploy artificial intelligence with confidence, transparency and regulatory alignment. Every model deserves scrutiny before it touches a decision.
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Our engagement follows a structured path from discovery through to ongoing governance. Each phase produces deliverables your compliance, engineering and leadership teams can act on immediately.
We map your AI estate — cataloguing every model, data pipeline and downstream integration. The output is a risk-ranked inventory that tells you exactly where to focus first. We interview data scientists, product owners and compliance officers to understand both technical architecture and business context before a single line of audit code runs.
Each model undergoes bias testing across protected characteristics, robustness probing with adversarial inputs, and explainability analysis using SHAP, LIME and counterfactual methods. We benchmark performance against your stated fairness thresholds and flag any drift from training distributions. Statistical significance tests ensure our findings are not artefacts of sample size.
Findings are translated into a prioritised action plan with severity ratings, estimated engineering effort and regulatory references. We do not simply hand over a report and walk away — our engineers pair with your team to implement fixes, retrain models and redesign data pipelines where necessary.
Post-deployment, our lightweight monitoring agents track model performance, data drift and fairness metrics in real time. Alerts fire when thresholds are breached, and quarterly re-audits ensure ongoing compliance with evolving regulation such as the EU AI Act and UK AI Safety Institute guidance.
Six specialised service lines, each backed by proprietary tooling and a team that has collectively audited over three hundred production models across financial services, healthcare, recruitment and public sector.
We test models against demographic parity, equalised odds and calibration metrics. Our reports detail which features drive disparate outcomes and quantify the trade-off between fairness and accuracy so decision-makers can choose informed thresholds rather than arbitrary ones.
Black-box models become transparent through feature-attribution dashboards, natural-language explanation generators and counterfactual reports that satisfy both technical reviewers and non-technical stakeholders including regulators and end-users.
We craft adversarial inputs — perturbed images, manipulated text, edge-case tabular records — to expose fragile decision boundaries. Results feed directly into retraining strategies that harden your model before an attacker finds the same weaknesses.
Garbage in, governance out. We trace every training sample back to its source, verify licensing and consent, detect label noise and measure representation gaps across sensitive groups. Clean data is the foundation of trustworthy AI software.
Whether you are preparing for the EU AI Act, meeting FCA expectations on algorithmic decision-making, or aligning with NIST AI RMF, we map your current posture against the relevant framework and close the gaps with documented evidence packs.
Deployed models decay. Our monitoring agents detect concept drift, feature drift and prior-probability shift in near real time. Automated alerts and monthly health reports keep your operations team ahead of degradation curves.
Unvalidated AI software creates measurable financial, legal and reputational exposure. We quantify that exposure and reduce it systematically.
Most single-model audits complete within three to four weeks. Enterprise-wide engagements covering multiple models and data pipelines typically span eight to twelve weeks depending on complexity, access to documentation and team availability for interviews.
Not necessarily. We offer both white-box and black-box audit modes. In black-box mode we interact with your model through its API, sending structured queries and analysing outputs. White-box access allows deeper explainability analysis but is not a prerequisite for a meaningful audit.
Financial services, healthcare, recruitment technology, insurance, public-sector decision support and e-commerce personalisation are our primary verticals. The methodology is model-agnostic, so we adapt to any domain where algorithmic decisions carry material consequences.
You receive a detailed audit report with severity-rated findings, a remediation roadmap with estimated effort, model cards for each audited system, an executive summary suitable for board presentation, and — if you opt for continuous monitoring — access to our real-time dashboard.
Engagements are scoped on a fixed-fee basis after an initial discovery call. We do not bill by the hour, which means no surprises. Continuous monitoring is offered as a monthly retainer scaled to the number of models and data volume under observation.
Tell us about the models you want validated and we will respond within one working day with a scoping proposal.
Office
50 Olson Side, Castle Stoltenbergworth, England, VS53 2WV, United Kingdom
Telephone
+44 7201 430761
Email
[email protected]
We typically reply within four hours during UK business hours (09:00–17:30 GMT). For urgent matters please call directly.
Effective: 06 Mar 2026
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Last revised: 2026-03-06
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Valid from 6/3/2026
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Audit outcomes depend on the quality and completeness of data, model access and documentation provided by the client. Passing an assurance audit does not guarantee regulatory approval or the absence of all model risk.
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