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QA Tech Xperts, Expert Testing, Trusted Results

Delivery Stories

Results,Not Just Test Reports

How we build Automation frameworks and AI quality practices that teams keep using after we hand them over.

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Playwright Frameworks AI Evaluation Harnesses Real Deliveries, Honestly Told
Test Automation · SaaS

Playwright Framework Built From Zero to CI-Gated Releases

The Challenge

A product team relied on slow Manual regression before every release. Coverage was undocumented, releases were delayed, and confidence in 'green' was low.

What We Built

A Playwright + TypeScript framework designed for maintainability, page-object architecture, stable data-testid selector strategy, parallel execution, and CI integration so every merge runs the suite automatically.

Senior Automation lead + Automation Engineer Framework foundation delivered in the first weeks, coverage grown sprint by sprint

The Results

  • · Regression that took days of Manual effort now runs automatically on every merge
  • · Flake-resistant selector strategy keeps green builds trustworthy
  • · Framework handed over with documentation, the client's team can extend it themselves
Read the full case study
AI Quality Engineering · Internal BuildInternal build

How We Engineered Our Own AI Evaluation Platform

The Challenge

AI features fail differently: hallucinations, prompt regressions, and RAG retrieval errors that no traditional test suite flags. A wrong answer returns HTTP 200. We needed a repeatable way to measure whether an LLM change made things better or worse before we could credibly offer that to anyone else.

What We Built

QEAP, our AI Quality Engineering harness: LLM output evaluation, prompt regression Testing, RAG retrieval accuracy checks, grounding verification and hallucination detection, packaged so it can be stood up inside a client's pipeline rather than run as a service by us.

AI QA practice lead + QA Engineer Built in-house, extended as the practice has grown

The Results

  • · LLM changes are measured rather than eyeballed — every prompt change runs the evaluation suite
  • · RAG retrieval accuracy is tracked against a golden dataset, re-run on every reindex
  • · Grounding and format checks run before an AI change can merge
  • · The harness is what we stand up on client engagements, not a demo we keep to ourselves
Read the full case study

Why there are two case studies here and not twenty

Most of our work sits under NDA, and we will not write around that with anonymised composites, invented logos or a wall of grey rectangles.

Most of our work sits under NDA, and we will not write around that with anonymised composites, invented logos or a wall of grey rectangles. So there are two write-ups here, and they are not the same kind of thing. One is a client engagement, described without naming the client and without figures. The other is our own internal build, labelled as such — we engineered our AI evaluation harness before offering it as a service, and we would rather show you that honestly than dress it up as somebody else's project.

You will also notice something missing: percentages. Results on this site stay qualitative until a client signs off on a number in writing, and none currently have. Vendors quote conversion lifts and defect reductions with a confidence that rarely survives contact with the underlying method, and we would rather be checkable than impressive. If you want figures, ask on a scoping call and we will tell you what we can substantiate and what we cannot.

What the two write-ups have in common

On the surface they are different problems.

On the surface they are different problems. One is a Playwright framework built for a client, replacing days of Manual regression with a gate that runs on every merge. The other is our own AI evaluation harness, which turns 'the assistant seems fine' into numbers that fail a build when they move the wrong way.

Underneath, both follow the same three commitments. Establish trust before scale, because coverage nobody believes is worse than no coverage at all. Define done as your own Engineer doing the work unaided, which changes what gets built and how it is documented. And leave the capability behind rather than the dependency: the framework, the harness, the dataset and the runbooks belong to you, in your repositories, under your licence.

How to read these if you are evaluating vendors

If neither engagement resembles your situation, the service pages describe how each discipline is run in general, and a free assessment produces written findings about your specific setup at no cost.

If neither engagement resembles your situation, the service pages describe how each discipline is run in general, and a free assessment produces written findings about your specific setup at no cost.

Look at the starting position, not the outcome. Both write-ups start from an ordinary, recognisable mess. If a case study starts from a well-organised baseline, it is telling you less than it appears to.

Check whether the architecture reasoning is specific. Selector contracts, API-driven data, golden dataset provenance, the details are where a real engagement differs from a template.

Read the handover section. Any vendor can build something. The question is whether your team can extend it in month nine without a support contract.

Note what is absent. No named clients, no unapproved metrics, no claims of sector experience we do not have — and where a write-up is our own build rather than a client engagement, it says so on its own page. That standard is set out on our industries and locations pages too.

Questions

Frequently Asked Questions

Straight answers, written the way we'd say them on a call.

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Where to go next

The practices these engagements came from.

Our commitments

What you can hold us to instead

We have no client quotes to show you — most of our work is under NDA and we will not invent the rest. These are commitments instead, which you can check against us.

01

You interview the Engineer before anything starts

Matching is our job, not a fait accompli. You can decline the person we put forward and we will match again.

02

No production access, ever

We work against masked or synthesised test data. If a finding can only be confirmed in production we hand it to your team with reproduction detail rather than asking for keys.

03

No recruitment or placement fee

Embedded Engineers are billed as a monthly rate. If an engagement ends, it ends — there is no exit charge and no conversion fee.

04

Everything we build is yours

Frameworks, harnesses, datasets and runbooks live in your repositories under your licence. Done means your Engineer extending it without us.

See what a handover contains
05

We will tell you if you do not need us

A free assessment produces written findings whether or not there is an engagement in it. If the answer is that you do not need us yet, that is what the findings will say.

Get a free assessment
06

No number we cannot substantiate

Results stay qualitative until a client approves a figure in writing. There are no percentages anywhere on this site, and that is deliberate.

Why our case studies have no metrics
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  • Within one business day, every time
  • NDA available before you share any details

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16

Testing disciplines

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Markets served

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