QAI Engineering
A methodology.
Quality engineering, re-engineered for the age of AI. QA professionals equipped with AI skills — and the strategy to embed AI into your organisation, alongside the traditional testing that still matters.
What QAI Is
One Discipline, Three Dimensions
QAI isn’t a tool we bolt on. It’s how we test, who we are and what we build inside your organisation.
AI embedded in how software is tested
Traditional quality engineering with AI built into every stage — from test design to release decisions — plus the validation of AI-enabled products themselves.
- AI-assisted test design & intelligent test generation
- AI woven into automation frameworks & CI/CD pipelines
- Validation of AI-powered products & LLM outputs
- Traditional QA fundamentals, never displaced — amplified
AI Enablement
How We Help Organisations Adopt AI
Most organisations know they need AI but don’t know where to start. We help you build it properly — across three layers, from infrastructure to the interface your teams use every day. At the centre: a skills.md for every workflow.
A skills.md for every workflow
Layer two isn’t a training deck or a one-off workshop. It’s a library of structured skills.md files — one for every testing and delivery workflow in your organisation. Each file defines how AI should operate: the inputs it needs, the steps it follows, the guardrails it respects and the outputs it produces. Your QAI Engineers author them; your executors run them; your teams govern them.
- skills.md authored for every workflow in your quality function
- Consistent AI behaviour — same steps, same standards, every time
- Workflow mapping across test design, regression, triage & release
- QAI Engineers who write, maintain and evolve your skill library
- Coaching internal teams to extend and govern skills responsibly
Workflow skills library
One skills.md per workflow
Every workflow in your quality function gets its own skills.md — a structured definition your executors load and your teams govern. Same format, same rigour, every time.
Test design & generation
Coverage analysis, case design and intelligent test generation.
Regression & automation
Suite maintenance, self-healing tests and pipeline quality gates.
Exploratory testing
Session charters, risk-based exploration and findings capture.
Defect triage & analysis
Clustering, root-cause surfacing and prioritisation.
Release readiness
Evidence-based go/no-go assessment and stakeholder reporting.
LLM & AI product validation
Output validation, hallucination checks and model evaluation.
API & integration testing
Contract validation, service mapping and integration coverage.
Performance & resilience
Load modelling, bottleneck analysis and non-functional assurance.
The Shift
Testing, Before and After QAI
Toggle between the two worlds. The fundamentals stay — everything around them gets faster, sharper and more confident.
Test design
AI-assisted design & intelligent generation
Regression
Self-maintaining suites that learn from change
Defect analysis
AI-clustered triage that surfaces root causes
Coverage
Risk-based, data-driven, continuously measured
Release decisions
Evidence-based confidence, sprint by sprint
AI products
Validated for accuracy, bias, security & hallucination
The QAI Engineer
Anatomy of a QAI Engineer
Four layers of capability in one engineer. Select a layer to see what’s inside.
The bedrock: engineering-grade test automation and everything a technical SDET brings.
- Test automation frameworks
- Playwright & Cypress
- API & integration testing
- CI/CD quality gates
- Performance & accessibility
Testing AI Itself
When Your Product Is the AI
AI-enabled products fail in new ways. We validate them with the same engineering rigour we apply to everything else.
LLM Outputs
Automated validation of large-language-model and chatbot responses — correctness, consistency and tone.
Accuracy & Hallucination
Measuring where models are right, where they invent — and how often, before your users find out.
Bias, Security & Privacy
Probing for biased outcomes, prompt injection, data leakage and the risks unique to AI systems.
AI Governance
Guardrails, auditability and accountability — AI within your delivery, under control and evidenced.
“We use AI to strengthen quality — not to replace skilled QA professionals. AI augments expert judgement; it does not substitute for it.”
The Novaeris principle
Engineering confidence into digital delivery
Ready to Release with Greater Confidence?
Whether it’s quality engineering, automation, delivery assurance, AI-ready testing or sourcing the right people — let’s talk about where you are and where you want to be.