GroveAI
Audit

AI Systems Audit

An independent, structured review of your AI systems. Find out what is working, what is not, and what needs to change.

Once AI systems are in production, they need scrutiny. Models drift, data distributions shift, edge cases emerge, and business requirements evolve. An AI systems audit provides an independent, structured assessment of your deployed AI — covering performance, accuracy, bias, data handling, security, and alignment with the business objectives the system was built to serve. We review the full stack: the data pipeline feeding the model, the model or API itself, the integration layer, the monitoring in place, and the human oversight processes around it. We test with real-world scenarios, adversarial inputs, and edge cases to surface problems before they become incidents. The output is a clear, prioritised set of findings with severity ratings and practical remediation recommendations. This is not an academic exercise — it is a practical health check that gives you confidence in what you have deployed and a clear action plan for what needs fixing.

Use Cases

What this looks like in practice

Production AI Health Check

Systematic review of AI systems that have been running in production. Assess performance degradation, data drift, and whether the system still meets its original objectives.

Pre-Launch Review

Independent assessment before a new AI system goes live. Catch issues in staging that would be costly to fix in production.

Third-Party AI Vendor Audit

Evaluate AI solutions provided by vendors or partners. Assess accuracy claims, data handling practices, and contractual compliance.

Post-Incident Investigation

After an AI-related incident — a bad prediction, biased output, or data leak — conduct a structured investigation to identify root causes and prevent recurrence.

Regulatory Preparation

Prepare for regulatory scrutiny by auditing AI systems against relevant standards and building the evidence base regulators expect to see.

Technology

Tools we work with

Model Evaluation FrameworksStatistical TestingPythonJupyter NotebooksMLflowWeights & BiasesData Profiling ToolsFairness MetricsPerformance BenchmarksAnthropic ClaudeOpenAI EvalsLangSmith

How It Works

Our approach

01

Scope & Access

Define audit scope, agree on access to systems, data, and documentation

02

Technical Review

Examine data pipelines, model architecture, integration points, and monitoring

03

Testing & Evaluation

Run performance tests, bias checks, edge case analysis, and adversarial probes

04

Findings & Severity Rating

Document all findings with severity ratings, evidence, and root cause analysis

05

Report & Remediation Plan

Deliver a clear report with prioritised recommendations and remediation guidance

Starting from

£8K

Timeline

1-2 weeks

Ready to get started?

Book a free strategy call and we'll assess whether this service is the right fit for your business.