Total Quality in AI

Build, deploy and scale AI with confidence

Talk to a Total Quality expert

Reduce bias, fairness and ethical risks with clear AI governance and assurance

Create clear ownership and accountability across the AI lifecycle

Assure AI across data, models, prompts, applications and operations

Engineer trust into AI, not just performance

If you are moving from AI pilots to production-scale AI, model accuracy is only one part of the challenge. You also need evidence that your AI systems are reliable, explainable, secure, fair, compliant and safe to operate across real-world workflows.

We help you build Total Quality into AI. We combine AI assurance, Quality Engineering, cyber security, data validation, model testing, regulatory alignment and continuous monitoring to help you move from experimental AI to trusted, governed and lower-risk AI adoption.

What we offer

Assuring AI with Total Quality

Build trusted AI with end-to-end assurance across the AI lifecycle.

Assuring AI with total Quality Advisory

Create reliable, secure and audit-ready AI through independent assurance advisory.

Governance, Risk and Compliance in AI

Scale AI confidently with governance, risk and compliance assurance.

Start with AI risk, then scale assurance with control

Total Quality in AI does not need to slow your innovation. We help you identify where AI risk sits, strengthen the foundations and scale assurance as your AI systems move deeper into your business.

ASSESS

You identify your AI use cases, model types, data dependencies, risk exposure, regulatory obligations and current assurance maturity.

Outcome: A clear baseline for what needs to be governed, tested and evidenced first.

ASSURE

You validate data quality, model behaviour, prompt performance, security controls, fairness, explainability and operational readiness.

Outcome: Greater confidence that your AI systems behave as intended before they scale.

SCALE

You embed continuous AI assurance into MLOps, SDLC workflows, monitoring, governance and release decision-making.

Outcome: A repeatable assurance model for deploying and operating AI with control.

The ecosystem that turns AI assurance strategy into delivery

You can use Resillion’s frameworks, platforms and accelerators to make AI assurance repeatable, measurable and practical across your delivery lifecycle.

Gives you a structured way to govern AI assurance across data, model quality, responsibility, safety, operations and monitoring, while aligning activity to requirements such as the EU AI Act, ISO 42001, GDPR and NIS2.

Result: A clearer control model for trustworthy AI.

Expert monitoring AI model validation and risk assessment on AI governance platform

Helps you assess AI systems against regulatory, data, model, safety and operational readiness requirements, so you can manage risk, assurance coverage and evidence across the AI lifecycle.

Result: Stronger visibility of AI risk, evidence and progress.

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

Speeds up AI assurance with pre-built assets for data validation, model validation, bias and fairness testing, behavioural boundary analysis and hallucination detection.

Result: Faster assurance with more consistent coverage.

Developers building AI agent orchestration and workflow automation solutions

Helps you create, manage and monitor AI agents and agentic workflows with configurable human-in-the-loop controls, so you can scale AI-enabled delivery without losing governance.

Result: Controlled scale for AI-enabled delivery and assurance.

Helps your teams use AI agents to support requirements analysis, test planning, test design, automation, execution, maintenance and reporting across repeatable QE activities.

Result: More consistent execution across repeatable quality activities.

Connects quality signals across the SDLC into stakeholder-specific views, so delivery teams, leaders and governance stakeholders can see risk, readiness and performance more clearly.

Result: Better decisions based on connected quality evidence.

Professionals exploring AI transparency and responsible AI assurance platform solutions

Helps you build role-based AI assurance capability across quality, automation, performance, security and red-team specialists through structured learning and on-the-job coaching.

Result: A scalable skills model for responsible AI assurance.

CASE STUDY

AI Quality Assurance at scale for a telecoms provider

A global telecommunications provider engaged Resillion to address growing quality and release‑risk challenges across a complex estate of customer‑facing digital services, where manual testing, inconsistent automation and rapid change were limiting scale and confidence.

Resillion introduced AI‑enabled quality and assurance approaches, including targeted automation optimisation, intelligent test prioritisation and stronger governance controls to stabilise delivery and reduce reliance on manual effort.

The result? This enabled the organisation to improve test consistency, scale quality more effectively and support faster, more reliable releases while maintaining service performance and customer experience across a large, fast‑moving digital environment.

WHY US?

Why choose Resillion for Total Quality for AI?

Quality built into the AI lifecycle

You can assure AI from strategy and design through data, models, prompts, applications, deployment, monitoring and continual improvement.

Connected expertise across QE, cyber and compliance

You get quality engineering, security testing, data assurance, model validation, regulatory alignment and operational monitoring brought together in one assurance approach.

Faster assurance through AI-enabled assets

You can use Resillion’s AI Assurance Platform, accelerators, agentic workflows and Quality Intelligence to reduce manual effort while keeping expert oversight and governance in place.

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40 %

Unvalidated AI models can lose up to 40% accuracy over time due to model drift

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80 %

80% of AI projects fail to scale beyond pilot without the right quality, assurance and governance foundations

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.

Find out more

Conor Thomson

Expert in Quality Engineering and AI Practices

Conor is a Global Solution Architect with 12 years’ experience in Quality Engineering, QA, test automation, software delivery, AI engineering, and digital transformation.

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FAQ’s

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.

Why do we need AI assurance if our models already perform well?

Strong model performance does not prove that an AI system is safe, fair, secure, explainable or ready for production. AI assurance tests how the system behaves across data changes, edge cases, adversarial inputs, user journeys, regulatory obligations and live operating conditions, so performance claims are backed by practical evidence.

Will AI assurance slow down innovation?

It should do the opposite. By identifying risks earlier, automating repeatable checks and embedding assurance into your AI and software delivery lifecycle, you reduce late rework, governance delays and production uncertainty. The goal is faster, safer scaling, not extra process.

Can Resillion help if our AI estate is still immature?

Yes. We can start with assessment, discovery and prioritisation, helping you understand your use cases, risk profile, data readiness, governance gaps and assurance maturity before you commit to larger-scale change.

How do you test for AI risks such as bias, hallucination and drift?

We combine data validation, model evaluation, prompt testing, behavioural boundary analysis, fairness checks, hallucination detection, security testing and continuous monitoring. The exact approach depends on your AI system, use case, risk level and regulatory exposure.

Does Total Quality in AI cover regulatory requirements?

Yes. Resillion can align AI assurance activity to relevant governance and compliance requirements, including the EU AI Act, ISO 42001, GDPR and NIS2, while helping you connect controls to practical assurance evidence across the AI lifecycle.

Can you work with our existing AI, MLOps and testing tools?

Yes. Resillion is tool-agnostic. We assess your current toolchain, keep what works and improve how your AI, quality, security, data and governance evidence connects, so you can strengthen assurance without unnecessary disruption.

How does human oversight fit into AI assurance?

Human oversight is central to the approach. AI can accelerate analysis, testing, reporting and monitoring, but assurance decisions need expert review, accountable governance and clear approval points, especially for regulated or high-impact AI systems.

ON-Demand WEBINAR

Supercharging AI assurance with Test Data Automation

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

Watch the recording

Get in touch

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.

Total Quality in AI

Engineer trust into AI, not just performance