QA for AI Products That Ship Every Week

Functional testing, regression, and adversarial evaluation for LLM-powered apps, copilots, and AI agents — scoped to your release cadence, delivered in 5–7 days.

Duration: 5–7 days Team: 2 AI QA Engineers
The Challenge

You might be experiencing...

You are shipping AI product updates weekly but have no systematic regression testing to catch when a model update breaks expected behaviour.
Your AI copilot or agent has shipped to customers and you are discovering failure modes reactively — from customer complaints.
Your enterprise customers require a QA report as part of procurement — you need structured test evidence, not ad hoc testing notes.
You updated your system prompt or switched model providers and need to verify the product behaviour hasn't regressed.

AI product QA is the systematic testing of products built on AI/ML components — LLM apps, copilots, recommendation systems, AI agents — for functional correctness, regression stability, and adversarial resilience.

Our Approach

Engagement Phases

Day 1

Test Design

Map all AI product user flows, critical path interactions, and known failure modes. Design functional test cases, regression test suite, and adversarial test cases specific to your product's use case and risk profile.

Days 2–5

Test Execution

Execute functional test suite across all user flows. Run regression tests against previous release baseline. Execute adversarial test cases: prompt injection via user input, indirect injection via tool outputs, and goal-hijacking in multi-step flows.

Days 6–7

Report Delivery

Structured QA report with all test results, regression comparison, and adversarial findings. Pass/fail verdict with prioritised issue list and specific remediation guidance.

What You Get

Deliverables

Functional Test Report — all user flows tested with pass/fail results
Regression Comparison — behaviour delta between current and previous release
Adversarial Findings — prompt injection, indirect injection, and goal-hijacking vulnerabilities
UX Failure Mode Map — AI-specific UX issues: hallucination in context, refusal failures, inconsistency
Release Readiness Verdict — pass/fail with specific blocking issues identified
Regression Test Suite — reusable test cases for ongoing QA by your team
Expected Outcomes

Before & After

MetricBeforeAfter
Regression CoverageNo systematic regression testing — regressions discovered by customersFull regression suite — regressions caught before release
Release ConfidenceShip and hope — no pre-release QA evidenceRelease readiness report — ship with documented QA evidence
Enterprise ProcurementNo QA documentation for enterprise customer security reviewStructured QA report — passed enterprise procurement review
Technology

Tools We Use

Playwright / custom AI test harness Promptfoo Custom adversarial prompt libraries OWASP LLM Top 10
Common Questions

Frequently Asked Questions

Can you run AI product QA on a weekly cadence?

Yes — our most engaged clients use aiml.qa as their weekly AI product QA partner. We scope a standing sprint agreement: defined test scope, agreed turnaround (typically 48 hours for regression-focused sprints), and a cumulative test report that builds over releases. Monthly retainer pricing makes this more cost-effective than individual sprint pricing.

What AI product architectures do you test?

Single-turn chatbots, multi-turn conversational AI, RAG-based knowledge assistants, AI copilots embedded in SaaS products, autonomous agents with tool use, and multi-agent orchestration systems. Each architecture introduces distinct failure modes — we test for the specific vulnerabilities relevant to yours.

How do you handle non-deterministic AI output in testing?

Non-determinism is the central challenge of AI product QA. We use a combination of: (1) temperature=0 for reproducible test runs where supported, (2) statistical sampling — running each test case N times and reporting pass rate rather than binary pass/fail, (3) semantic equivalence evaluation — checking whether outputs satisfy the requirement semantically rather than matching exact strings. All methodology is documented in the report.

Ship AI You Can Trust.

Book a free 30-minute AI QA scope call with our experts. We review your model, data pipeline, or AI product — and show you exactly what to test before you ship.

Talk to an Expert