AI testing tools amplify what you have.
They don't replace what's missing.
A 2–4 week fixed-price engagement to evaluate, implement, and integrate the right AI-assisted testing tool for your stack — Playwright AI, Mabl, or Applitools — embedded into CI and handed over with a process your team can run.
The pitch for AI testing
is usually overstated.
AI-assisted testing tools are genuinely useful — for teams that already have a working QA practice. They can reduce the maintenance burden of existing test suites, catch visual regressions with less manual effort, and surface test coverage gaps automatically. Used in the right context, they're a real force multiplier.
What they don't do: replace a test strategy, magically produce reliable automation for teams without a QA foundation, or run themselves without governance. The failure mode is teams adopting an AI testing tool because it seems modern, spending time on integration, and producing the same quality of output they had before — just faster and with a monthly subscription.
This engagement evaluates whether you're in the right position to benefit from AI-assisted testing, picks the right tool for your specific situation, implements it properly, and puts a governance process in place. If you're not ready, Stuart will tell you that — and recommend what to do first.
Three tools. Different strengths. One choice.
AI-enhanced scripting
For teams already using Playwright. AI-assisted test generation, smart locators, and auto-healing. Enhances an existing Playwright suite without replacing it or introducing a new platform dependency.
Low-code AI testing platform
ML-driven test creation and maintenance that adapts as the UI changes. Best for teams with limited code-first automation experience who need a maintainable regression suite. Lower code overhead; higher platform cost.
AI visual testing
AI-powered visual regression detection. Integrates with existing Playwright/Cypress/Selenium suites. Best for teams who need to catch UI regressions at scale without writing assertions for every visual change.
Evaluate. Implement. Integrate. Hand over.
Evaluation
Week 1. Assess your existing QA foundation, identify the highest-value use case for AI tooling, and confirm the right tool choice. Written recommendation with rationale before implementation begins.
Implementation
Weeks 2–3. Integrate the selected tool with your existing suite or build the initial AI-generated test coverage. Configuration, calibration, and baseline establishment.
CI integration & governance
Final week. Integrate into CI pipeline. Establish the review and governance process — who reviews AI-flagged results, what constitutes a pass or fail, how false positives are handled.
Handoff
Team walkthrough covering the tool, CI integration, governance process, and how to expand coverage. Documentation delivered. Stuart's involvement ends.
AI testing tools require a foundation
to build on.
Before this engagement, you should have: a defined QA strategy, a working test suite your team trusts, and CI integration already in place. Without those, an AI testing tool adds complexity without adding confidence.
Has a working test suite
Existing automation that runs in CI, the team trusts, and catches real regressions. AI tooling amplifies this.
Clear use case identified
Knows what the AI tool is meant to solve: visual regression coverage, maintenance burden, or test generation speed.
No existing automation
QA Foundations or Test Automation Build first. AI tooling without a foundation produces AI-generated noise.
✓ Visual regression testing is too expensive to maintain manually
✓ You want a structured implementation, not a proof-of-concept
Fixed-price. Tool-licence costs are separate.
Does not include tool licence costs (Mabl, Applitools). These are billed directly to you by the vendor. Stuart's engagement fee covers evaluation, implementation, CI integration, and handoff.