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Adopt AI

AI adoption for QA teams: tool choice, a measured pilot and training

Your QA team has been asked to use AI, and the tools are easy to try and hard to judge. You get a ranked list of where AI fits your testing, a pilot on a real suite with a record of what held up, written rules for working with AI output, and a team trained to follow them.

Request an assessment quote How an engagement runs
pilot review log, checkout suite, AI-assisted test drafts 2 passed 1 failed 1 review
  • AI draft of a login test Reviewed by an engineer; assertions meaningful accepted
  • AI draft of a payment test Asserts only that the page loads rewritten
  • Locator repair after a UI change Fix matches the intended element accepted
  • Draft for a flow with test data setup Needs a decision on data ownership open
  • Founders of CodeceptJS and Codeception
  • Founders of Testomat.io and Testomat.ai
  • Since 2015
  • 4.9 out of 5 on Clutch
What it is
AI adoption consulting for QA teams
First step
AI readiness assessment About one week
You receive
Adoption candidates ranked by value and risk +2 more
After that
Adoption pilot Two to six weeks

What we test

Services in this area

  • AI readiness assessment

    Where AI helps your team today, where it does not, and what has to change first.

    • Current process, suite health and skills reviewed with your leads
    • Candidate tasks ranked by value and risk
    • Written recommendation, including what to leave alone

  • Tool selection

    A shortlist chosen against your stack, your data rules and your budget, tried on your own code before anyone signs a licence.

    • Criteria agreed first: languages, CI, data handling, review effort
    • The same real task given to each candidate
    • A decision record your team can reread in a year

  • Measured pilot

    One product area, one goal, a fixed period. The team uses AI on live work, and every AI output is logged as accepted, changed or rejected.

    • Goal and stop criteria written before the pilot starts
    • Time spent on review counted, not only time saved on drafting
    • A decision based on the log, not on impressions

  • Working rules and training

    Written rules for how the team uses AI, and hands-on sessions on your own code so people can follow them.

    • What may be sent to a model, and what may not
    • The review standard for AI-drafted tests and fixes
    • Who owns a test once AI has drafted it
Measured AI adoption pilot with a review logA loop for each task in the pilot. Task: a real task from your suite, for example drafting a regression test. Tool under trial: the AI tool drafts the output. Engineer review, the gate: the output is accepted, changed or rejected. Review log: one line per output, with the reason and the review time. The loop repeats for every AI output used during the pilot. Decision: at the end of the pilot, the decision is made from the lines in the log.Your teamTaskA real task, forexample aregression testfor the couponflowAI agentTool undertrialDrafts the outputEngineerEngineerreviewAccepted, changedor rejectedYour teamReview logOne line peroutput, with thereason and thereview timeYouDecisionMade from the logat the end of thepilotRepeated for every AI output used during the pilotMeasured AI adoption pilot with a review logA loop for each task in the pilot. Task: a real task from your suite, for example drafting a regression test. Tool under trial: the AI tool drafts the output. Engineer review, the gate: the output is accepted, changed or rejected. Review log: one line per output, with the reason and the review time. The loop repeats for every AI output used during the pilot. Decision: at the end of the pilot, the decision is made from the lines in the log.Your teamTaskA real task, for example a regression testfor the coupon flowAI agentTool under trialDrafts the outputEngineerEngineer reviewAccepted, changed or rejectedYour teamReview logOne line per output, with the reason andthe review timeYouDecisionMade from the log at the end of the pilotRepeated for every AI output used during thepilot
How a measured AI adoption pilot works: every AI output is reviewed by an engineer and logged, and the decision about the tool is made from the log.

Why adopting AI in QA goes wrong

The usual pattern is not a failed tool. It is a team that tried several tools with no shared goal and no way to tell whether any of them helped.

  • Trials without a measure. Everyone has an opinion about what the assistant did, and nobody has a log.
  • Output nobody owns. An AI-drafted test is merged because it is green. Months later no one knows what it checks.
  • No rules for data. People paste code, logs or customer data into a tool because no one said not to.
  • Review time ignored. Drafting gets faster; reading the drafts takes longer than expected, and nobody counted it.

The fix is ordinary engineering discipline applied to a new tool: a goal, a bounded pilot, a review step and written rules.

What the review log records

Every AI output used during the pilot gets one line. The decision at the end is made from these lines.

Field Example
Task Draft a regression test for the coupon flow
Tool The assistant under trial
Outcome Accepted, changed or rejected
Why Assertion only checked that the page loaded
Review time Minutes an engineer spent reading and fixing it

How it fits your team

We work inside your repository, CI and review process, with your engineers. The pilot’s changes go through your normal pull requests. When the engagement ends, the rules, the review log and the decision record stay with you.

For a wider look at your test process before any AI work, see QA audit and assessment; for the strategy side, test automation strategy; and for stand-alone courses, trainings.

How an engagement runs

Start small, with a fixed scope

Each step has a defined scope, duration and deliverables. You decide after each one whether to continue.

  1. Step 1 About one week

    AI readiness assessment

    One team and one suite. We review how testing is done today, try a few AI tools on a sample of your real work and say where to start and where not to.

    You get

    • Adoption candidates ranked by value and risk
    • Tool shortlist with the criteria behind it
    • Pilot scope, goal and stop criteria
  2. Step 2 Two to six weeks

    Adoption pilot

    Your engineers use the chosen tools on one product area, with us alongside. Training sessions run during the pilot, and the working rules are written as the team learns what holds up.

    You get

    • Pilot report built from the review log
    • Working rules for AI use in testing
    • A recommendation: roll out, adjust or stop
  3. Step 3 Monthly

    Rollout support

    Zapple.Tech engineers help carry what worked into other teams and product areas, and revisit the rules and tool choices when the tools change.

    You get

    • Rollout plan per team
    • Rules and tool choices revisited when a tool changes
    • Monthly report on adoption and review findings

My developers really enjoyed the trainings and consulting done by Zappletech. We found the trainer very competent. Thank you guys!

Jakub Polomsky Technical Module Owner, Austria More reviews

FAQ

Questions buyers ask

Do we need an existing automation suite?

A suite helps, because the pilot then runs on real work. If you have little automation, the assessment tells you whether to build the base first or start with AI-assisted manual and exploratory testing.

Which AI tools will you recommend?

Whichever fits your stack and your data rules after a hands-on trial on your own code. Zapple.Tech's founders also founded Testomat.io and Testomat.ai; if one of them is on the shortlist, the report says so and shows the same criteria applied to the other candidates.

What happens to our code during the tool trials?

Only tools your team approves are tried, on code you choose. If your rules allow no code to leave your environment, the trial is limited to tools that run inside it.

Is this training or consulting?

Both, and kept practical. Sessions use your code and your tools. If you only want a stand-alone course, the trainings page lists the courses.

How long until we know whether AI helps?

The assessment takes about a week. The pilot runs two to six weeks; the length is set by how many sprints it takes to collect a review log large enough to decide on.

Who runs the sessions?

Zapple.Tech test automation engineers. Two or three of your engineers take part throughout, so the knowledge stays in your team.

How much does it cost?

Prices are not published. The quote depends on the size of the team, the number of tools to trial and how many training sessions you want. Describe your team and suite and you get a scoped quote within one business day.

Start with a one-week AI readiness assessment

Tell us about your team, your stack and the AI tools you have already tried. We reply within one business day with scope, timeline and a quote.

  • Founded by the founders of CodeceptJS and Codeception, open-source testing frameworks
  • Founded by the founders of Testomat.io and Testomat.ai
  • On the market since 2015
  • Rated 4.9 out of 5 on Clutch, from 7 client reviews

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    We reply within one business day.