Know Your AI QA Risks Before You Ship

A 3-day structured audit of your entire AI stack — models, data, and products — with a prioritised risk register and sprint recommendations.

Duration: 3 days Team: 1 Senior AI QA Engineer
The Challenge

You might be experiencing...

You are shipping an AI product to enterprise customers who ask about your QA process — and you don't have a formal answer.
Your model was trained 6 months ago. You don't know if it has drifted or whether your evaluation metrics still reflect real-world performance.
You are preparing for a Series B and investors have asked about AI risk — hallucination rate, bias testing, data quality — and you have no documentation.
You have internal QA but no one on your team has run a formal ML model evaluation before.

The AI QA Readiness Assessment is the fastest way to understand your AI quality risk — and the entry point for every aiml.qa engagement.

What the Assessment Covers

Most AI teams have some form of evaluation. Few have a systematic view of their QA coverage across all three layers where AI systems fail:

Model layer — Is your model evaluated beyond accuracy? Bias testing, fairness across demographic subgroups, robustness to adversarial inputs, and edge-case coverage are routinely absent from internal evaluations.

Data layer — How was your training data collected, labelled, and validated? Data quality issues are the most common root cause of silent model failures in production — and the hardest to detect after the fact.

Product layer — If your model powers an AI product, is the product tested end to end? Functional regression, prompt injection surface, hallucination rate in context, and UX failure modes require product-level QA that model-level evaluation doesn’t cover.

Why Start Here

The Readiness Assessment gives you three things you can’t get from ad hoc testing:

  1. A risk register — not a list of things to check, but a prioritised register of actual risks in your specific stack, ranked by severity and likelihood.
  2. A maturity score — a baseline you can report to investors, customers, and regulators, and improve over time.
  3. A sprint roadmap — the exact QA work that addresses your top risks, scoped and ready to execute.

For teams preparing for Series A/B fundraising, enterprise customer procurement, or regulatory review, the executive summary deliverable provides external validation documentation that internal testing cannot.

Our Approach

Engagement Phases

Day 1

Stack Inventory & Risk Mapping

Structured review of your AI stack: models in production, training data sources, evaluation methodology, MLOps pipeline, monitoring coverage, and AI product surface area. We map every component against a risk matrix.

Day 2

Evaluation & Gap Analysis

Hands-on review of model evaluation artefacts, data quality indicators, test coverage, and production monitoring. We identify gaps between your current QA state and what is required for your risk profile.

Day 3

Report & Sprint Recommendations

Delivery of a structured QA Risk Register: every finding categorised by severity, root cause, and recommended remediation. Sprint recommendations map each risk to the specific aiml.qa service that addresses it.

What You Get

Deliverables

AI QA Risk Register — every finding ranked by severity (Critical / High / Medium / Low)
Current-state QA maturity score across 5 dimensions: model evaluation, data quality, AI product testing, MLOps pipeline QA, and monitoring coverage
Sprint recommendations — specific services mapped to your top 3 risks
Executive summary — 1-page format suitable for board, investor, or procurement use
30-minute debrief call to walk through findings and answer questions
Expected Outcomes

Before & After

MetricBeforeAfter
Time to First QA InsightNo formal QA process — unknown risk profileStructured risk register delivered in 72 hours
Investor ReadinessNo AI risk documentation for due diligenceExecutive summary suitable for Series A/B investor review
Sprint ROIAd hoc testing with no prioritisationTop 3 risks identified — targeted sprint scope saves 40%+ vs. undirected QA
Technology

Tools We Use

Custom QA Maturity Framework Model Evaluation Checklist (50+ criteria) Data Quality Rubric OWASP LLM Top 10
Common Questions

Frequently Asked Questions

What access do you need to run the assessment?

We work from documentation, artefacts, and a structured intake questionnaire — we do not require direct access to your model weights, training data, or production systems. For teams comfortable sharing more, we can review evaluation notebooks, data pipeline code, and monitoring dashboards directly. The assessment is designed to be low-friction and fully async — most teams complete the intake questionnaire in under 2 hours.

What is the price of the AI QA Readiness Assessment?

USD 2,500 for a 3-day assessment with full deliverables. This is our entry-point sprint — designed to be low-friction to purchase and high-value as a standalone deliverable. Payment by Stripe or invoice. No MSA required for this engagement.

What happens after the assessment?

You receive a QA Risk Register with sprint recommendations. You choose whether to act on any of them — there is no obligation. For teams that proceed, the assessment fee is credited against the first sprint engagement. Most clients find that the Risk Register alone changes how they prioritise their QA investments.

Is the assessment suitable for pre-launch AI products?

Yes. Pre-launch is often the most valuable time to run it — before technical debt around QA compounds. We assess your intended architecture and data pipeline alongside any existing artefacts, and deliver a QA roadmap timed to your launch milestones.

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