QA & Validation for AI Solutions
Independent quality assurance, validation and audit of AI systems — developed in-house or by another vendor. We assess correctness, safety, security, compliance and ROI, and deliver a structured findings report with a prioritized remediation roadmap. There is no requirement to have built the system with us.
Independent: an objective, expert review of AI delivered by any team or vendor
Full transparency: we trace inputs, decisions and actions end to end — never a black box
Governance-ready: aligned to AI TRiSM, the EU AI Act, GDPR and DORA
Why validate your AI — even when another vendor delivered it
An AI system that answers questions is straightforward to demonstrate and difficult to trust in production. The moment an agent begins to act — updating a record, approving a request, issuing a communication — an incorrect or unlogged decision becomes a tangible operational and compliance risk. Many systems are delivered without an audit trail, without access control on tool calls, and without a validation step to intercept errors before they reach a customer. Exelor identifies these gaps before they become incidents. As an organisation that also builds production AI systems, we understand precisely where they tend to fail.
Independent review
An objective second set of expert eyes on AI delivered by any internal team or third-party vendor
Evidence, not opinion
Structured tests generate the evidence; evaluation interprets it against agreed quality criteria and recognised frameworks
Governance-ready deliverables
Findings mapped to AI TRiSM, the EU AI Act, GDPR and DORA — defensible to auditors and stakeholders
Proportionate to risk
Testing rigour scaled to the system’s potential impact — exhaustive for critical decisions, lightweight for low-risk tools
What we assess
Nine assessment dimensions, applicable to rule-based systems, machine-learning models, generative AI and autonomous agents alike.
Assessments are benchmarked against recognised frameworks — the NIST AI Risk Management Framework, the OWASP Top 10 for LLM applications, AI TRiSM, the EU AI Act and the UK Cross-Government AI Testing and Assurance Framework — so that findings are defensible and evidence-based rather than subjective.
An evaluation-driven approach
AI cannot be assured the way traditional software is: it behaves probabilistically, learns from data that shifts over time, and may act autonomously. Our method separates three layers of confidence. Testing exercises the whole system — infrastructure, integrations, data flows and interfaces — to generate evidence. Evaluation examines the model layer against agreed quality attributes. Assurance combines both into a defensible judgement that the system is safe, fair and accountable. The rigour of each layer is proportionate to the system’s risk profile.
Rule-based systems
Rule coverage, boundary conditions, fallback behaviour and audit of which rules fired for each decision
Machine-learning models
Hold-out metrics, stability under input perturbation, drift indicators and group-level fairness analysis
Generative AI & LLMs
Output quality and factuality review, adversarial and jailbreak prompt suites, guardrail and moderation checks
Agentic AI
Safety of autonomous actions, behaviour in novel scenarios, override and kill-switch verification, decision traceability
Engagement models
Three levels of assurance for different risk profiles, timelines and budgets.
AI Health Check
A rapid, proportionate review delivered in days. A scored assessment of the principal risks and priority improvements, suited to a pre-launch checkpoint or vendor sign-off.
Days · Pre-go-live checkpoint
Full QA & Validation Audit
Comprehensive testing across every dimension above, accompanied by a risk register, an evidence pack and a prioritized remediation roadmap mapped to AI TRiSM and the EU AI Act.
Weeks · Enterprise / critical systems
Continuous QA
Ongoing validation, regression testing and monitoring as the system and business requirements evolve — drift detection, scheduled re-testing and periodic compliance reviews.
Ongoing · Live systems
How an engagement is delivered
01
Scope
We agree the system under review, the risks of greatest concern and the success criteria
02
Assessment
We map the architecture and execute structured tests across the dimensions above
03
Findings
& risk register
Each issue is rated by severity and likelihood and supported by evidence
04
Remediation
roadmap
A prioritized set of corrective actions — implemented by us or handed to your team or vendor
05
Re-test & sign-off
We verify the corrections and confirm production readiness with an evidence-backed recommendation
This mirrors the Audit and Measure stages of our EAAF framework — the same control-first methodology we apply when building. Where remediation warrants a rebuild, our Custom AI Agents Platform and AI automation and orchestration services continue the work.
Validate your AI before production
Get an independent, evidence-based read on correctness, safety, security and compliance
FAQ
Can you assess an AI system another vendor developed?
Yes. Independent review is the core of this service. Exelor acts as an objective assessor of AI developed in-house or by a third party, with no requirement to have built it, and provides an evidence-based assessment together with a clear path to resolution.
What deliverables do we receive?
A findings report with a severity-rated risk register, supporting evidence and a prioritized remediation roadmap, followed by a re-test and sign-off once corrections are in place. Deliverables are mapped to AI TRiSM, GDPR, DORA and the EU AI Act, so they are defensible to auditors and stakeholders.
How long does an AI QA audit take?
An AI Health Check is typically completed within days. A full validation audit runs over several weeks, depending on system complexity and the number of integrations. Scope is agreed in advance so that the effort remains proportionate to the risk.
What types of AI systems can you validate?
Rule-based and deterministic systems, machine-learning models, generative AI including LLM applications and RAG pipelines, and agentic AI that acts autonomously. The testing focus is adapted to each: rule coverage for deterministic logic, statistical evaluation and drift for ML, adversarial prompt suites for generative systems, and safety of autonomous behaviour with override verification for agents.
Does every system receive the same depth of testing?
No — rigour is proportionate to risk. A high-impact system that makes decisions about people or money warrants exhaustive testing across all dimensions; a low-risk internal assistant can be assured with a lighter, focused review. The initial scoping step classifies the system’s risk and sets the appropriate depth, so effort is concentrated where the potential impact is greatest.
Do you provide assessment only, or remediation as well?
Both. We can transfer the remediation roadmap to your team or the original vendor, implement the corrections ourselves and re-test, or undertake a rebuild on our controlled, auditable AI platform where that is the more appropriate course.
Obtain an independent assessment of your AI
Share the AI system you would like reviewed — its function and where it operates — and we will scope a proportionate QA engagement. You will receive a clear, evidence-based answer to a single question: is this system safe to trust in production?