AI assurance
Independent assurance for responsible, safe, and effective AI
How we assure AI
AI systems are only as reliable as the way they’re tested and evaluated. Our AI assurance services are aligned with the UK Government AI Playbook and AI Testing Framework, and use structured risk-based AI testing to help you strengthen governance, manage risk, and build confidence in your AI systems.
Our approach is human-led and independent, focused on how your systems perform in real-world conditions. We align our approach with emerging regulatory expectations and integrate directly with your governance and risk processes, helping you build assurance that stands up to scrutiny.
Assurance across the AI lifecycle
We provide assurance from early design through to live operation and ongoing use. We define testing strategies and risk-based approaches early, then assess data quality, evaluate model behaviour, and test performance within integrated environments.
Before release, we carry out pre-deployment evaluation, including operational acceptance testing, to confirm readiness and validate compliance through AI compliance testing against your defined requirements. Once live, we support ongoing monitoring to identify drift, performance issues, and unintended behaviour.
What we test
We provide evaluation across the areas that most influence how your AI behaves in practice.
Understand how your AI performs under real conditions.
We assess how models perform across a range of realistic scenarios, including accuracy, consistency, and unintended behaviours. This includes exploring how outputs vary with different inputs, helping identify instability, unexpected responses, and potential risks before they reach production.
This gives you a clearer view of how your model behaves under pressure, supporting confident deployment decisions.
Ensure the data behind your AI is reliable, representative, and fit for purpose.
We review the quality, structure, and suitability of the data that informs your AI, identifying issues such as bias, gaps, or inconsistencies. By examining how data is prepared and used, we help reduce the risk of flawed outputs and highlight areas for improvement in your AI risk assessment.
Stronger data foundations lead to more consistent outputs and greater trust in your system.
Validate how your AI behaves when it connects with the wider system.
Verify that your AI performs reliably when connected to wider systems and services
We test how AI behaves when connected to APIs, interfaces, and wider systems, including integration testing for AI systems and potential regression risks. This helps uncover issues that may only emerge when components interact, ensuring your AI continues to perform reliably once deployed within live systems.
This reduces the risk of unexpected failures when your AI is introduced into complex, real-world environments.
Track how your AI changes as it moves from development into real use
We monitor for drift, instability, and changes in performance as models are updated or exposed to new data. This ongoing visibility helps maintain consistent performance as conditions change and systems evolve, giving you confidence in your AI performance evaluation over time.
Ensure your generative AI produces reliable, safe, and controlled outputs
Ensure your generative AI produces reliable, safe, and controlled outputs
Generative AI introduces risks such as hallucinations, unsafe outputs, and data leakage - making targeted AI testing essential. We test prompts and outputs across varied prompts and real usage scenarios to assess consistency, relevance, and safety, including how the system responds to edge cases and unexpected inputs.
This helps you maintain control over outputs and reduce the risk of harmful or unintended content reaching users - a key part of responsible AI assurance.
Models and outputs
Understand how your AI performs under real conditions.
We assess how models perform across a range of realistic scenarios, including accuracy, consistency, and unintended behaviours. This includes exploring how outputs vary with different inputs, helping identify instability, unexpected responses, and potential risks before they reach production.
This gives you a clearer view of how your model behaves under pressure, supporting confident deployment decisions.
Data and inputs
Ensure the data behind your AI is reliable, representative, and fit for purpose.
We review the quality, structure, and suitability of the data that informs your AI, identifying issues such as bias, gaps, or inconsistencies. By examining how data is prepared and used, we help reduce the risk of flawed outputs and highlight areas for improvement in your AI risk assessment.
Stronger data foundations lead to more consistent outputs and greater trust in your system.
System integration
Validate how your AI behaves when it connects with the wider system.
Verify that your AI performs reliably when connected to wider systems and services
We test how AI behaves when connected to APIs, interfaces, and wider systems, including integration testing for AI systems and potential regression risks. This helps uncover issues that may only emerge when components interact, ensuring your AI continues to perform reliably once deployed within live systems.
This reduces the risk of unexpected failures when your AI is introduced into complex, real-world environments.
Behaviour over time
Track how your AI changes as it moves from development into real use
We monitor for drift, instability, and changes in performance as models are updated or exposed to new data. This ongoing visibility helps maintain consistent performance as conditions change and systems evolve, giving you confidence in your AI performance evaluation over time.
Generative AI
Ensure your generative AI produces reliable, safe, and controlled outputs
Ensure your generative AI produces reliable, safe, and controlled outputs
Generative AI introduces risks such as hallucinations, unsafe outputs, and data leakage - making targeted AI testing essential. We test prompts and outputs across varied prompts and real usage scenarios to assess consistency, relevance, and safety, including how the system responds to edge cases and unexpected inputs.
This helps you maintain control over outputs and reduce the risk of harmful or unintended content reaching users - a key part of responsible AI assurance.
If you’re developing or deploying AI systems, we can help you build assurance into the process - let's talk.
'I’ve worked with many other external QA companies, but they only do as they’ve been asked. Zoonou go way beyond that – they are proactive with suggestions and ideas on how to improve processes. That’s what makes this such a great partnership.'
- David Comer, Software Test Manager at Blatchford Prosthetics
Independent assurance for risk and governance
Our independent assurance helps you understand and manage risk with clarity and control. Working separately from development teams, we give you an objective view of how your AI stands up to your risk frameworks, policies, and regulatory requirements - free from internal bias or assumptions. This provides clear, evidence-based insight into performance, so you can make informed decisions, maintain control, and demonstrate compliance with confidence through AI compliance testing.
We’ve supported organisations across regulated sectors, including healthcare and the public sector, to test and assure complex digital systems. Our approach builds on this experience, applying the same independent, risk-based testing methods to AI and ML systems.
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BlatchfordAccelerating a health tech manufacturers digital transformation journey
Accelerating a health tech manufacturers digital transformation journeyAutomationStrategy -
Southern HousingSupporting Southern Housing through multiple transformation programmes
Supporting Southern Housing through multiple transformation programmesPublic SectorStrategy -
Cyclr SystemsQA automation framework that cut regression testing time by 77%
QA automation framework cut regression testing time by 77%Automation
Build trust with independent AI assurance
Get clarity on how your AI performs and the confidence to move forward.
Your questions answered
What’s the difference between AI testing and AI assurance?
AI testing focuses on evaluating specific elements of a system, such as models, data, or integrations.
AI assurance brings these elements together into a structured, independent assessment of risk, governance, and overall system performance - helping you understand not just how your AI behaves, but whether it is safe, compliant, and fit for purpose.
When should we use AI assurance?
AI assurance is most valuable when you need independent insight into how your system performs in practice.
You should consider it when you're:
- preparing to release an AI system.
- deploying into a regulated environment.
- scaling existing models.
- validating outputs against risk and compliance requirements.
It’s also useful for assessing and reducing risk in systems already in use.
How does your approach align with UK government frameworks?
Our approach is derived from the UK Government AI Playbook and AI Testing Framework.
This ensures our work follows recognised best practice for AI governance, risk management, and responsible development - helping you demonstrate compliance and accountability.
How does your assurance services support risk and governance?
We combine independent AI performance evaluation with structured risk and compliance assessment against your defined risk frameworks, policies, and regulatory requirements.
This provides clear, evidence-based insight that supports auditability, strengthens governance, and helps you demonstrate that your AI systems meet the standards and controls required in regulated environments.
How do you work with our team?
We provide independent AI assurance alongside your delivery, without being part of the development process. This gives you an objective, external view of how your systems perform while your teams retain full ownership of development and delivery.
Our role is to support your internal processes with structured AI testing and evaluation. You can find out more about how we work and how we help you make informed decisions, strengthen control, and manage risk without disrupting delivery.
What other assurance services do you provide?
Alongside AI assurance, we support related services including:
- System integration testing,
- Operational acceptance testing
- ERP testing
- Penetration testing
- WCAG accessibility compliance audits
These help ensure your systems perform reliably within wider environments, meet operational requirements, and remain secure against potential risks.
Can you support AI assurance before deployment?
Yes. We support pre-deployment AI testing and assurance to help identify risks, validate performance, and confirm readiness before systems go live.
Do you work with regulated sectors?
Yes. We work with organisations across regulated sectors, including healthcare and the public sector.
Our independent approach is designed to meet the requirements of these environments, providing clear, evidence-based assurance that supports governance, compliance, and confident decision-making.