AI and Machine Learning Services
of organisations cite data accuracy or bias as one of the biggest barriers to AI adoption.
The real risk with AI isn’t that it fails. It’s that it appears to be working until it matters most.
Priya couldn’t explain why the model made that decision
Marcus couldn’t see the risk building across the data, model and live environment
Elena thought testing had already caught the bias
Arjun still had no single view of whether the AI was ready to go live
Organisations are racing to adopt AI and machine learning to drive efficiency, automate decisions and unlock new value from data. But as AI becomes embedded into core systems, it introduces a new level of complexity.
What starts as innovation quickly becomes exposure. Because AI isn’t static.
It continues to learn, adapt and influence outcomes long after deployment.
Without the right controls, testing and governance in place, organisations risk making faster decisions, but with less certainty.
What if you could…
Trust AI decisions with explainable, validated outputs aligned to real-world outcomes
Identify risks early through continuous testing, monitoring and assurance
Improve data quality, reduce bias and build models on stronger foundations
Scale AI innovation safely without increasing risk exposure
Maintain control across the full AI lifecycle, from development to live operation
Trusted by leading organisations
Helping organisations improve quality, resilience and delivery confidence across complex digital estates.
Our approach to AI testing services and quality assurance
We help organisations adopt and scale AI with confidence, combining advisory, quality engineering and continuous assurance into a single, joined-up approach.
From AI readiness assessments and use case design to implementation, testing and optimisation, we support the full lifecycle.
We design governed, scalable AI solutions, embed them into delivery pipelines, and apply independent assurance to validate performance, security, bias and compliance. Continuous monitoring ensures models remain reliable as they evolve.
We help you to deliver AI that is trusted, controlled and proven to deliver real business value.
How we can help
If you’re curious to find out more about our AI and Machine Learning services take a look at some of these pages:
End-to-end assurance that makes AI trusted, governed, and reliable across the full lifecycle, from data and model validation to continuous monitoring in production.
Using AI to accelerate and modernise testing, automating insight and diagnosis so teams deliver faster with higher confidence.
CASE STUDY
AI in software testing: Interpreting test diagnosis for 600 tests per day
A European telecoms/media provider ran thousands of automated Android Live TV playback tests daily. Even a small failure rate created significant manual triage, with engineers reviewing recordings, screenshots and logs and relying on specialist knowledge to pinpoint root causes across DRM, app updates and environment issues.
We added an AI analysis layer to a Test‑as‑a‑Service platform to automatically interpret device interactions, external video capture for DRM-protected playback and test artefacts (logs, timelines, screenshots).
The result: The system now analyses ~350–400 tests per day (scaling to 600) at ~95% accuracy, accelerating diagnosis and reducing manual effort.
Why Resillion for AI and Machine Learning?
700+ global experts
700+ specialists across quality engineering, testing and cyber security
End-to-end AI lifecycle coverage
Supporting AI from readiness and design through to testing, deployment and live operation
AI-powered quality engineering
Using AI to accelerate testing, improve quality and expand coverage across the SDLC
Independent AI assurance
Proven capabilities in bias testing, model validation, explainability and security testing
Trusted, regulation-ready AI
Supporting compliance with frameworks such as the EU AI Act and governance requirements
Continuous monitoring and control
Continuous evaluation of models for drift, instability and real-world performance
Data and model validation at scale
Testing data quality, bias and model behaviour across complex systems
Flexible delivery model
Advisory, implementation, testing or managed services aligned to your needs
Applied AI in complex environments
Delivering AI-enabled testing and assurance in large-scale, real-world systems
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.
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.
