AI Quality & Readiness Assessment

The problem

Traditional testing assumes deterministic output: same input, same result, pass or fail. Model-backed features break that assumption. The output is a distribution, the failure modes are semantic rather than structural, and the cost of a bad response is reputational or regulatory rather than a stack trace.

Most teams respond by bolting more end-to-end tests onto the front of a non-deterministic system. It produces a flaky suite that everyone learns to ignore, and it tells you nothing about the failures that actually matter: confidently wrong answers, prompt injection, data leakage, cost blow-outs and silent quality drift after a model update.

What these features need is an evaluation architecture, not more E2E tests. I have built one for our own product, PixellPeep, which is the reason this offer exists.

What the assessment covers

• Evaluation architecture. What a golden dataset for your use case should contain, which metrics are meaningful, and where deterministic assertions still apply versus where they cannot.
• Regression safety. How you detect quality drift when a prompt, a retrieval index or a model version changes — before customers do.
• Guardrails. Input validation, output constraints, refusal behaviour, prompt injection exposure, and what happens when the model is unavailable or slow.
• Data handling. What leaves your boundary, what is retained by providers, and whether that is defensible to an enterprise security review.
• Observability. Tracing, logging and human review loops sized so that failures are visible and diagnosable in production.
• Unit economics. Cost per interaction under real usage, the shape of the curve at 10×, and where cost controls belong.
• Enterprise readiness. The questions an enterprise buyer's security and compliance team will ask about your AI feature, and whether you can currently answer them.

How it runs

Week 1 — System read. Architecture of the AI feature, prompt and retrieval design, current testing and monitoring, incident and complaint history, and a working session with the engineers who built it.

Week 2 — Failure hunting and write-up. I probe the feature against the failure classes above and document what I find, then deliver a written assessment and a live readout. The findings that matter are usually the ones nobody had a test for.

What you receive

• A written AI quality and readiness assessment with a clear go / go-with-conditions / not-yet position
• A prioritised risk register — each finding tied to a commercial or compliance consequence
• A proposed evaluation architecture: dataset design, metrics, thresholds and where it sits in CI
• A guardrail and observability specification your team can implement
• A cost model for the feature at current and projected usage
• The enterprise-buyer question list, with your current answer next to each

Who this is for

SaaS and product teams shipping their first serious AI feature, teams whose AI feature is already live but whose quality nobody can currently measure, and companies facing an enterprise security review that covers AI usage. Usually 10–200 engineers, with at least one engineer who will own the evaluation system afterwards.

Who this is not for: teams still exploring whether to build anything with AI — prototype first, assess when there is a system to assess.

Investment

Fixed fee for a two-week engagement, quoted after a scoping call. Many teams continue onto the advisory retainer once the assessment surfaces decisions that keep recurring; implementation, where wanted, is scoped separately or built with Aarohii.