Assuring AI with Total Quality Advisory

Create safe, accurate machine learning models

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Trusted by leading organisations

Helping organisations improve quality, resilience and delivery confidence across complex digital estates.

Making AI Reliable, Secure and Audit-Ready

With the frenetic pace at which AI is being adopted, governance tends to fall behind, leading to trust issues, security exposure and weak evidence for oversight. With our proven track record that can circumvent such concerns, our independent AI assurance advisory can increase confidence in your AI use case while minimising operational or regulatory risk.

With a clear approach that begins with conducting risk assessment and assurance design, all our assurance activity provides clear, defensible evidence that can be used for audits and regulatory activities. As a result, the delivery of AI applications that are reliable, secure, explainable and fit for purpose across the full lifecycle becomes a reality.

Enhance your applications and platforms with AI quality assurance advisory

Our approach to AI quality assurance advisory first requires a baseline understanding of both the risks involves and the assurance activities necessary to deliver reliable, predictable models. Along with this, validating data quality, model behaviour as well as the functional, non-functional and security aspects of AI-infused applications are key capabilities that we offer. While governance and assurance evidence go together, continuous monitoring of the models validates their reliability and safety after go-live.

Otherwise known as Total Quality, our integrated, governance-led approach delivers quality engineering, cyber security assurance, conformance and interoperability aligned with industry-grade automation and AI accelerators.

Our teams use a range of tools and strategies to build continuous quality in early, tailored to your development environment, delivery model and risk profile. This helps ensure your machine learning models, AI-infused applications and platforms are fit for purpose, regardless of use case, function or industry.

Professional validating digital assurance and software QA testing approval on smart interface

BENEFITS

How Resillion assures AI with Total Quality advisory for best results

We’ve proven that assuring AI with Total Quality offers quality outcomes such as:

Reliable and predictable AI behaviour in the real-world

Reduced exposure to bias, fairness and ethical failures

Regulation-ready evidence that stands up to scrutiny

Security and adversarial testing for AI enabled systems scaled

Lower operational disruption

Joined up assurance no silos fewer gaps scaled

Stronger security across AI data, models and interfaces

How we turn capabilities into results scaled

Confidence to scale AI without introducing hidden risk

Lower cost of assurance and fewer late-stage failures

WHY US?

How we turn capabilities into results

Here’s how Resillion’s teams help you assure AI in a way that stands up to scrutiny:

AI risk profiling and assurance planning scaled

AI risk classification and assurance design

What this does for you

You identify where AI introduces material risk across data, models, decisions and outcomes

Result

Enables faster, safer and predictable AI adoption

Data quality, bias and lineage assessment

What this does for you

You verify whether AI data is accurate, representative, traceable and ethically sound

Result

Reduced risk associated with biased, inaccurate and unethical AI outcomes

Model behaviour validation (incl. prompt/output evaluation where relevant)

What this does for you

You understand how AI models behave under expected and edge-case conditions

Result

Increases confidence that AI behaves predictably under real-world conditions

AI-specific security and adversarial assurance

What this does for you

You address security specific risks such as data leakage, model abuse, adversarial input and unsafe outputs

Result

Reduced exposure to AI-related security threats, and increases overall security posture

Operational monitoring for drift and instability scaled

Continuous monitoring for drift, degradation and instability

What this does for you

You validate whether AI models are exhibiting model drift, performance decay or behavioural change

Result

Maintains long-term confidence by detection of silent deterioration of AI systems

Audit-ready assurance evidence and reporting

What this does for you

You have access to traceable, execution-based evidence on AI risks

Result

Reduces audit disruptions so as to build confidence with boards, regulators and customers

WHY NOW?

Still hesitating? See what’s at stake

If you’re not yet assuring AI with Total Quality, consider what you may be up against without it:

Undetected model drift

Unreliable AI outputs

Loss of stakeholder trust

Weak audit evidence

Compliance gaps

Delayed approvals

Prompt/pipeline exposure

Data leakage risk

Security incidents

Our experts

Robby Putzeys

Head of Quality Engineering

Robby Putzeys is a seasoned leader with over 25 years of experience in software quality engineering, helping organizations deliver high-quality IT systems, digital products, and complex technology landscapes across industries such as telecommunications and consumer electronics.

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Robin Klusman

As the Head of Cybersecurity Solution Architecture, Robin leads a team of senior architects dedicated to maximising business impact through robust security solution design.

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FAQs

Didn’t find the answer you were looking for? Our team is happy to help just reach out and we’ll get back to you soon.

How is assuring AI different from testing traditional software?

AI introduces probabilistic behaviour, model drift, data dependency and new attack surfaces - so assurance must cover data, models, prompts/outputs, and the systems AI is embedded into, not just functional testing.

We're already testing our AI models. Why do we need AI assurance as well?

Traditional testing verifies whether an AI system functions as expected at a point in time. AI assurance goes further by evaluating data quality, model behaviour, bias, explainability, security exposure and real-world outcomes. This helps you understand not only whether your AI works, but whether it can be trusted to perform reliably and responsibly in production.

When should AI assurance begin in our AI lifecycle?

The earlier the better. AI assurance is most effective when introduced during planning and design, before models reach production. By assessing AI risks, data quality and governance requirements early, you can address issues before they become costly operational, regulatory or reputational problems later in the lifecycle.

How does Assuring AI with Total Quality help us manage AI risk?

Total Quality helps you address risks such as biased outcomes, poor data quality, unpredictable model behaviour, security vulnerabilities, data leakage, weak explainability and regulatory non-compliance. By bringing quality engineering, governance and security together, you gain a joined-up assurance model that identifies and manages risks before they impact your users or business operations.

Our AI solution is already live. Is it too late to benefit from AI assurance?

No. AI systems continue to evolve as data, user behaviour and operating environments change. A Total Quality approach introduces continuous monitoring and ongoing assurance activities that help you identify model drift, degraded performance, security risks and unexpected behaviours long after deployment.

Where do you start if we’re early in our AI journey?

Start with an AI risk classification and assessment, then build a proportionate assurance plan across data, models, applications and operations - aligned to your regulatory and business context.

Can you help with EU AI Act readiness?

Yes. Resillion supports AI risk classification and assessment, readiness activities, and assurance evidence that helps organisations demonstrate control, traceability and ongoing monitoring.

How does the Total Quality approach help with AI governance and emerging AI regulations?

A Total Quality approach generates traceable, evidence-based assurance that shows how AI risks have been identified, tested and controlled throughout the lifecycle. This helps you demonstrate accountability, support internal oversight and provide the evidence needed for audits, governance reviews and emerging AI regulatory requirements.

ON-DEMAND WEBINAR

Supercharging AI assurance with Test Data Automation

Is your test data ready for scalable AI assurance under NIS2?

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Providing solutions in a unified, structured approach across all critical domains to enable end-to-end quality – Because we’re the only ones who can.

Assuring AI with Total Quality Advisory

Create safe, accurate machine learning models

Trusted by leading organisations

Making AI Reliable, Secure and Audit-Ready