AI Assurance Platform
Assure, accelerate and scale responsible AI adoption
AI systems can create regulatory, operational and reputational risk if they’re not properly tested and monitored. Resillion’s AI assurance platform gives you a managed way to assure your data, models and AI outputs, with our AI assurance framework and reusable accelerator assets applied behind the scenes to structure delivery, build evidence and support confident AI adoption.
Trusted by leading organisations
Helping organisations improve quality, resilience and delivery confidence across complex digital estates.
Assure AI across data, models, governance and operations
Resillion’s AI assurance platform helps you test and monitor AI systems from end to end. It is supported by our AI assurance framework and reusable AI assurance accelerators, which our teams apply as internal delivery assets to cover data, models, LLMs, AI safety, operational readiness and continuous monitoring. This gives you the evidence you need to reduce risk and meet requirements such as the EU AI Act, NIS2, ISO/IEC 42001 and GDPR.
Rather than relying on policies alone, the AI assurance platform brings structured assessment criteria, reusable test assets, risk classification, evidence and ongoing assurance into one practical, managed approach.
Guided by our Total Quality approach, our experts apply these assets alongside the tools that best fit your organisation, embedding AI assurance early in development so you can build and deploy AI with confidence.
A structured framework for responsible AI assurance
Resillion applies its AI assurance framework as the delivery methodology behind the AI assurance platform.
Lifecycle assurance
Build assurance into each stage of AI delivery:
Requirements, data preparation, model development, validation, deployment, testing and monitoring.
Risk and controls
Prioritise assurance based on risk and impact:
Assessment criteria, risk classification and control design.
AI testing
Validate both expected performance and variable AI behaviour:
Functional, non-functional, user acceptance, data validation, model QA, bias, fairness and hallucination testing.
Cyber resilience
Identify threats to model integrity and output safety:
Data poisoning, prompt injection, credential leakage and OWASP AI Top 10 risks.
Regulatory alignment
Build evidence for regulators, auditors and governance teams:
EU AI Act, NIS2, ISO/IEC 42001, GDPR and related expectations.
Our framework structures how AI quality, reliability, fairness and trustworthiness are assessed across the full lifecycle, connecting governance, testing, cyber security and regulatory alignment so you receive risk-based controls, consistent assurance activity and clearer evidence for responsible AI adoption.
BENEFITS
How Resillion’s internal AI assurance assets support controlled, compliant adoption
At Resillion, our AI assurance platform is strengthened by our framework and accelerator assets, helping you achieve better quality outcomes such as:
Lifecycle-wide AI assurance
Risk-based controls and evidence
Faster, more consistent testing
Stronger data, model and LLM validation
Continuous monitoring and safer deployment
Scalable responsible AI capability
Accelerate AI assurance with reusable test assets
Resillion’s teams use AI assurance accelerators within the AI assurance platform to assess AI risk faster and more consistently. These reusable test assets, scripts and assurance patterns extend coverage across data, models, behaviours and outputs, while reducing the effort needed to build assurance evidence from scratch.
Data and model validation
What it supports:
Data quality, readiness, model performance and robustness.
How it helps you:
Validate AI foundations faster.
Bias and fairness testing
What it supports:
Unfair outcomes, skewed behaviour and representation gaps.
How it helps you:
Find fairness issues earlier
Behavioural boundary analysis
What it supports:
Edge cases, unusual inputs and operating limits.
How it helps you:
Find fairness issues earlier
Hallucination detection
What it supports:
Unsupported, inaccurate or misleading outputs.
How it helps you:
Improve trust in LLM responses.
Responsible AI evidence
What it supports:
Control evidence, reporting and documentation patterns.
How it helps you:
Build consistent audit evidence.
WHY US?
How we turn capabilities into results
Here’s how Resillion’s AI assurance platform turns our framework, accelerators and responsible AI capability into measurable business outcomes:
Lifecycle-wide AI assurance
What this does for you
You assess data, models, outputs, controls and monitoring across the AI lifecycle.
Result
End-to-end assurance coverage
Risk-based framework
What this does for you
You classify risk, define controls and connect assurance to key obligations.
Result
Stronger governance evidence
Reusable accelerator assets
What this does for you
You use repeatable assets to assess common AI risks faster.
Result
Faster, consistent delivery
Data, model and LLM validation
What this does for you
You test data quality, model performance, prompts and outputs.
Result
More trustworthy AI behaviour
Bias, fairness and boundary testing
What this does for you
You assess bias, fairness, boundaries and unsafe responses.
Result
Safer, fairer AI outputs
Continuous monitoring
What this does for you
You monitor drift, model change and control effectiveness
Result
Sustained operational confidence
Operational readiness
What this does for you
You validate controls, ownership and readiness before go-live
Result
Safer deployment
Responsible AI capability
What this does for you
You scale assurance through role-based learning and AI champions.
Result
Scalable assurance capability
Built to scale responsible AI delivery capability
AI is still a new and evolving technology, and building the right skills to assure AI development is vital. At Resillion, providing you with experts is our business. We have developed a Workforce Transformation methodology for responsible AI to build the skills, behaviours and delivery practices our teams need to operate the AI assurance platform effectively at scale. Through practical learning paths, AI Champions and live innovation initiatives, our engineers move from AI awareness to confident, responsible delivery, strengthening the capability behind your assurance outcomes.
WHY NOW?
Still hesitating? See what’s at stake
Here’s what can happen when AI assurance is not embedded early enough through a managed platform approach, supported by reusable testing assets and Responsible AI delivery capability:
AI decisions that are hard to explain when evidence is requested.
Bias, hallucinations or unsafe outputs reaching users.
Weak evidence causing delays, rework or scrutiny.
Data or model drift going unnoticed in live use.
Governance, testing and monitoring working in silos.
Teams lacking the skills to scale responsible AI assurance.
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.
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.
