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AI Quality Assurance: One QA Process for Your Entire AI Product

AI quality assurance covers every layer in a single process: what the AI says, how the features around it behave, and the software underneath — UI, data, integrations, performance, security, and accessibility. One engagement, with a QA engineer signing off on every judgment call.

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  • AI and QA together

    one process, one report, one team

  • Every change retested

    code, model, prompt, or retrieval logic

  • Human-reviewed

    a QA engineer signs off each judgment call

  • Certified

    ISO/IEC 27001:2022 and ISTQB Silver Partner

Trusted by teams shipping AI to production

  • Fisherman Labs
  • Fight camp
  • iiaa
  • Lunaphore
  • Solarflare
  • Viamo
  • wezz e-commerce
  • Acumen
  • Isadora
  • Group
  • Ewave logo
  • Brkfst logo
  • Anchor AI Logo

What is AI Quality Assurance?

AI quality assurance is a QA approach for products that contain AI or LLM components. It brings AI-specific work— LLM evaluation and LLM testing, — together with functional, regression, performance, security, and accessibility testing of the product as a whole. 

QA Madness provides AI quality assurance services for SaaS companies, AI product teams, and enterprise software vendors across the UK, USA, Canada, and Europe. 

This page covers QA for AI products as a whole. If you need a single layer tested rather than all of them, the specialist services are linked below.

 How is AI quality assurance different from AI product testing?

AI Product & LLM testing adds one focused AI layer to the QA you already run. AI quality assurance is the whole approach, for teams that want every layer covered and coordinated by one partner. 

The practical difference shows when a model update breaks something three layers away, and one team is accountable for finding it.

Traditional QA AI Quality Assurance
Scope UI, data, business logic, APIs Everything in traditional QA, plus AI outputs and LLM behaviour
Pass / fail Binary Binary for the deterministic parts, judgment against agreed criteria for the AI parts
Risks covered Functional, performance, security, accessibility Plus hallucination, context loss, prompt injection, output drift
Re-test trigger Code change Code, model, prompt, or data change

What layers does AI Quality Assurance include?

Four QA layers run in one process, so a model update, a prompt change, and a code release are all measured against the same quality bar.

By checking each layer continuously, we prevent silent failures before they reach production.

  • AI Output Quality

    LLM evaluation, faithfulness, answer correctness, safety of content, tone, completeness

    This page

  • LLM System Behaviour

    LLM testing, conversations, retrieval, tool use, prompt injection and adversarial safety, schema validity

    QA Madness

  • Regression & Automation

    Playwright and WebdriverIO for web, Appium for mobile, running in your CI/CD, with flakiness tracking and coverage gaps reported

    QA Madness

  • Functional, Performance, Security & Accessibility

    UI, data, business logic, API contracts, load, penetration testing, WCAG compliance

    QA Madness

We ship AI features ourselves — so we QA AI products end to end

QA Madness engineers build AI features into our own products, including QAM Hub, our test management platform. 

We see how an AI feature, the code surrounding it, and the release process interact, so we test them as a single system.

  • 13 years in quality assurance
  • 100% Middle and Senior engineers on every engagement
  • 81% Middle and Senior engineers on every engagement
  • ISO/IEC 27001:2022 certified
  • ISTQB Silver Partner

Who is AI Quality Assurance for?

  • You're building an AI-first product

    The AI is the core of what you sell, so quality has to cover the model and everything around it.

  • You don't have a QA process for AI yet

    You want one partner to set up and run quality across every layer, with a single scope and a single report.

  • You're operating in a high-stakes context

    Healthcare, finance, legal, or enterprise. One bad response can mean regulatory exposure, user harm, or an incident that takes months to recover from.

  • Enterprise buyers require quality and safety evidence

    Procurement and regulators now ask for documented due diligence across the whole product, usually late in a deal.

What does AI Quality Assurance cover?

7 areas in one AI product QA scope, reported together.

  • Output quality

    LLM evaluation

    We score AI outputs for faithfulness to your source content, answer correctness, safety of the content itself, tone, and completeness, against criteria agreed with you.

  • System behaviour

    LLM testing

    We test conversations, retrieval, tool use, and the integration of LLM-powered features, including prompt injection and adversarial safety, using scenarios mapped to the OWASP Top 10 for LLM Applications.

  • Safety

    Safety & prompt injection

    We verify the AI refuses harmful requests, protects sensitive data, and resists injection attacks, including instructions hidden in content users paste.

  • Functional

    Functional & integration testing

    UI, data, business logic, and API contracts of the product around the AI. The parts users still notice first when they break.

  • Regression

    Regression & automation

    Automated suites built in Playwright or WebdriverIO for web and Appium for mobile, running in your CI/CD, plus LLM-graded checks that flag drift when a model or prompt changes.

  • Performance

    Performance testing

    Load testing of the product and its AI-powered flows. We measure time to first token and streaming behaviour, handling of provider rate limits and timeouts, and token cost under load, using mocked model responses where hitting the real API would be costly.

  • Security

    Security testing

    Penetration testing of the application and its APIs.

  • Accessibility

    Accessibility testing

    WCAG compliance checks, including the interfaces where people interact with the AI.

How does QA Madness approach AI Quality Assurance?

Black-box, on the integration layer - no access to your model weights or training data required. We test the AI the way your users hit it.

  • Scope

    Risk-based scope

    We start from your product's highest-risk scenarios, AI and non-AI alike, and build coverage around them. The budget goes where the damage would be.

  • Reference sets

    Golden answers, agreed with you

    For AI outputs, "correct" becomes a written standard your team approved before scoring starts.

  • Scale

    LLM-as-a-judge, human-adjudicated

    Automated scoring for coverage at scale, calibrated against human-labelled samples first. A QA engineer then reviews every failure, every borderline score, and a sample of passes.

  • Adversarial

    Curated attack prompts

    Public jailbreak and injection datasets, extended for your domain and your data, mapped to the OWASP Top 10 for LLM Applications.

  • Regression

    One regression strategy

    Code, model, prompt, and retrieval changes all trigger re-tests of the affected scenarios. Nothing falls between two vendors' scopes.

  • Reporting

    Every finding, reproducible

    Input → actual output → expected → severity and business impact, in one report across all layers.

Automation is what gives us coverage at scale. 

Every result that involves judgment is reviewed and signed off by a QA engineer before it reaches your report.

How does an AI Quality Assurance engagement work?

No surprises. Clear milestones. Full transparency from kickoff to ongoing support

  1. Discovery Call

    We learn what your product does, which AI components it uses, how often you release, and which quality risks your team needs covered.

  2. QA Assessment

    Engineers review your documentation, AI system instructions, current test coverage, and known risk areas across every layer.

  3. QA Strategy & Test Plan

    We define what good AI output looks like for your product, then plan functional, regression, performance, security, and accessibility coverage around it.

  4. Execution & Reporting

    Engineers run the agreed scenarios. Every finding is documented with input, actual result, expected result, severity, and business impact.

  5. Ongoing Support

    We retest after fixes and keep regression running as your code, model, and prompts change. On project-based work, the scenario library and regression suite stay with your team.

Case Study

How evaluation exposed answer-quality risks in a B2B SaaS support assistant

A customer-facing assistant for a mid-market SaaS, built on an LLM via API with retrieval over the help centre. Functional QA was green. Nobody could say how good the answers were

  • 11

    hallucinated answers invented pricing tiers and a refund window that does not exist

  • 4

    prompt-injection bypasses assistant disclosed its system instructions on crafted input

  • 23%

    of multi-turn chats lost context forgot the product named two turns earlier

  • 3

    malformed-JSON outputs broke the downstream ticket-creation step

Outcome:

Roughly 3 weeks, added to the QA scope already running: 140 scenarios across 4 failure modes. Every high-severity finding was resolved before public launch, and the scenario library stayed with the team to rerun against the next model update.

Case Study

What does an AI QA finding look like?

AI and non-AI issues are reported in the same format, so your team can reproduce and fix them the same day.

Failure mode Cross-Layer Regression

Severity: High
Input
A prompt update shipped to make answers "more conversational"
Actual output
Average response length grew by roughly 40%. The chat panel began clipping answers on mobile, and two automated tests started failing on a text assertion.
Expected
A prompt change is a release. Its effect on layout, automated coverage, and downstream parsing is checked before it ships. UI tests assert structure and behavior, never exactly generated text.
Why it matters
Nothing was wrong with the prompt or the UI on their own. Three teams each saw a problem that belonged to someone else.

Failure mode Accessibility of the AI Interface

Severity: High
Input
Screen reader user sends a message to the assistant
Actual output
The reply is rendered into the page with no live-region announcement. The user hears nothing and cannot tell whether the assistant replied.
Expected
The response area is announced once, when the answer is complete.
Why it matters
The AI answered correctly. For this user the product is unusable, and it is the layer most AI QA scopes leave out.

What Clients Say About Working With Us

  • “QA Madness has established a smooth workflow through effective communication. The team is trustworthy, efficient, and hardworking.”
    Jonathan Lopinot Jon Lopinot CTO at BRKFST
  • “Thanks to QA Madness’s efforts, we are able to resolve technical issues and keep our platforms optimized and bug-free.”
    Marc Uitterhoeve Marc Uitterhoeve CEO at Dexter Agency
  • “QA Madness was seriously professional. They listened to our needs and gave us the kind of work we expected. As a result of their efforts, we can locate a bug in the test environment, which prevents issues from entering production. I would recommend them, 100%.”
    Alessandro Ronchi Alessandro Ronchi COO at Bitbull Srl
  • "They’ve always been very professional, prompt, and available when we needed them. We’ve never had any issues or needed to go back and teach them how to meet our standards."
    Alex Mathias Alex Mathias VP at Isadora Agency

AI Quality Assurance: Common Questions

Answered by QA Madness AI testing engineers

AI Quality Assurance is a QA approach for products with AI or LLM components. It combines AI-specific testing and evaluation with functional, regression, performance, security, and accessibility testing of the whole product.

LLM evaluation scores for AI outputs: faithfulness, answer correctness, content safety, tone. 

LLM testing assesses how the system behaves across retrieval, tool calls, adversarial safety, and schema validity. 

AI Quality Assurance includes both, plus traditional QA of everything around them.

AI product & LLM testing adds one AI-focused layer to your existing QA. AI quality assurance covers all layers in one process, coordinated by one team. This suits teams that don’t want quality split across several vendors. 

Functional QA confirms the feature works. It cannot tell you whether the AI’s answer is right, safe, or still stable after an update. Those need AI-specific checks on top of what you already run.

Yes. We test product behaviour whichever model or provider sits behind it.

Yes. RAG means the model answers from your own content, so we test the retrieval layer separately: the right source, a stale index, a missing source. For agentic features that take actions, we check which tool is called, with which arguments, and what happens when a step fails mid-chain. Both need your traces or logs, or a test endpoint, rather than the interface alone. 

We start with a POC of about six weeks: first automated checks and CI/CD integration within the first month, full smoke coverage by the end. From there, QA runs continuously as your code, model, and prompts change.

A focused cycle runs from kickoff to first findings in week 1, with the full report inside a single sprint.

QA Madness works with clients across the UK, USA, Canada, and Europe, from our headquarters in Warsaw, Poland.

Your AI Product is Built. Is Every Layer Tested?

Tell us about your AI product and we'll map the highest-risk scenarios across the AI and the software around it, then show you which QA scope makes sense.

No commitment required. Response within 1 business day

  • First automated checks in month 1
  • Human sign-off on every result
  • Independent of your dev team
  • Works with your existing QA flow
  • Reviewed on Clutch, Gartner, G2
QA Madness
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