Skip to content

Quality Assurance

Automation Testing Services

We build the automated test suite you can trust. A broad base of fast unit tests, focused integration tests, and a thin, stable layer of end-to-end checks, all running on every commit.

Book a consultationTalk to us

What Automation Testing means in practice

Who it’s for: Teams releasing often enough that manual regression testing has become the bottleneck, who want a fast, trustworthy automated suite built by engineers who know what is worth automating and what is not.

This is the service that gives you a test suite you can actually rely on, running automatically on every change. Not a folder of recorded clicks that goes red for reasons nobody understands, and not a coverage number that looks reassuring while the important paths go untested: a genuine safety net that runs in your pipeline, fails loudly when a change breaks something real, and stays green the rest of the time. When engineers trust the suite, they ship faster because a passing build actually means something. When they do not, they learn to ignore it, and you are paying to maintain a lie.

The heart of the work is the test pyramid: a broad base of fast unit tests that pin down logic in milliseconds, a focused middle layer of integration tests that check the seams between your code and the database, the queue and the third parties, and a deliberately thin top layer of end-to-end tests that drive the whole system through the browser to prove the handful of journeys that must never break. The proportions are the point. Get them right and the suite is fast, stable and cheap to keep; get them upside down. The classic ice-cream cone of too many slow, flaky end-to-end tests and too few unit tests, and you get a suite so slow and unreliable that the team stops believing it.

We build with modern tooling chosen for the layer: Playwright or Cypress for end-to-end, both of which handle the async nature of real web apps far better than older stacks and, for most new suites, beat Selenium (there is a dedicated Selenium technology page if you are tied to it); Jest, Vitest or pytest for unit tests; and Postman collections or REST-assured for API-level checks. Underneath the tool choice sits the discipline that actually keeps a suite alive: stable selectors instead of brittle CSS paths, the Page Object Model so a UI change is a one-line fix rather than a hundred, and test data managed deliberately rather than left to accumulate as shared, mutable state that makes every run depend on the last.

What you get

  • A test suite structured as a real pyramid. A broad base of fast unit tests, a focused band of integration tests, and a thin, deliberate layer of end-to-end journeys, not an ice-cream cone of slow browser tests
  • End-to-end coverage of your critical user journeys in Playwright or Cypress, built on stable, test-dedicated selectors and the Page Object Model so a UI change does not shatter half the suite
  • Unit and integration tests in Jest, Vitest or pytest that pin down business logic and the seams to your database, queues and third parties, with the slow and unreliable dependencies faked deliberately
  • API-level tests (Postman collections or REST-assured), that verify contracts and error paths directly, without paying the cost of driving a browser to check something an HTTP call can prove
  • The whole suite wired into your CI pipeline so every push and pull request is verified automatically, with a clear, fast signal on what broke and where
  • A test data strategy (factories, fixtures, seeding and teardown), so tests are isolated and repeatable rather than depending on a shared database that drifts between runs
  • A flakiness policy and the tooling to enforce it: quarantine for unstable tests, root-cause fixes rather than blind retries, and a suite the team is expected to keep green

What Automation Testing does for you

  • Faster, calmer releases

    The point of an automated suite is to compress the slow, repetitive part of releasing (re-checking that the things that worked yesterday still work today), from days of manual effort into a few minutes of pipeline time. When that check is fast and trustworthy, releasing stops being a nervous event you schedule and becomes something you do whenever a change is ready.

  • Bugs caught where they are cheap

    A defect caught by a unit test on a developer’s machine costs minutes to fix. The same defect caught in production costs an incident, a hotfix, and the trust of whoever hit it. A suite weighted towards fast, low-level tests catches most problems at the cheap end and reserves the expensive end-to-end checks for the few journeys where nothing else will do.

  • A suite that survives the codebase changing

    Most automated tests die not because they were wrong but because they were brittle, tied to fragile selectors and copy-pasted UI steps, so a routine design change turns the whole suite red. We build for maintainability from the start, with the Page Object Model and stable selectors, so that when the app changes the tests are cheap to update rather than a tax so heavy the team quietly deletes them.

Why teams choose us for Automation Testing

  • You want the pyramid built the right way up. A broad base of fast tests and a thin, deliberate layer of end-to-end checks, by engineers who have felt what the upside-down ice-cream cone costs in slow, flaky pipelines, not a suite of recorded browser clicks sold by the hundred.
  • You want an honest account of what to automate and what not to: the regression paths worth the investment, and the unstable UI or one-off checks where automation would waste money and manufacture flakiness. We would rather talk you out of automating something than bill you for a test that erodes trust.
  • You want modern, appropriate tooling. Playwright or Cypress for the browser, Vitest, Jest or pytest for the base, API-level tests where an HTTP call is enough: chosen per layer on your stack, with a straight answer on where Selenium still fits and where a newer tool wins.
  • You want automation that complements your testing rather than pretending to replace it. A green suite proves the things you told it to check; it does not explore, and it does not notice the unexpected. We are clear about that boundary and design the automated layer to free your people up for the manual and exploratory work only humans do well.

What Automation Testing includes

The concrete pieces of work this covers, scoped to what your problem actually needs.

  • End-to-end tests in Playwright and Cypress

    We build the thin top of the pyramid (the handful of full-system journeys that must never break), in Playwright or Cypress, chosen for your stack. Both auto-wait for the application to be ready instead of guessing with fixed sleeps, which is the single biggest source of the flakiness that plagues older browser automation. We keep this layer deliberately small, because every end-to-end test is slow to run and expensive to maintain, and its job is to prove the journey holds together, not to test every edge case a lower layer already covers.

  • The Page Object Model and stable selectors

    The difference between a suite that lasts and one that rots is almost entirely maintainability. We wrap each page or component behind a Page Object, so the details of how to log in or add to a basket live in exactly one place and a UI change is a one-line fix rather than a hunt through a hundred tests. And we select elements by stable, test-dedicated hooks (data-testid attributes and roles), never by brittle CSS or XPath paths that shatter the moment a designer nudges the layout.

  • Unit and integration tests in Vitest, Jest and pytest

    The broad base of the pyramid: fast, isolated unit tests that pin down business logic in milliseconds, and focused integration tests that verify the seams where your code meets the database, the message queue or a third-party API. We fake the slow and unreliable dependencies deliberately, so these tests run in seconds and fail for real reasons, and we resist the temptation to reach for a heavyweight browser test when a unit or integration test would prove the same thing far faster.

  • API and contract testing

    A large share of what teams check through the browser can be verified far more cheaply against the API directly. We build API-level tests (Postman collections, REST-assured or the equivalent in your language), that exercise endpoints, validate response schemas, and check the error and authorisation paths, not just the happy one. This layer sits between unit and end-to-end, catching whole classes of bug without the cost and fragility of driving a UI to reach them.

  • CI/CD integration and fast feedback

    A test suite that does not run automatically is a suite that rots. We wire yours into your pipeline (GitHub Actions, GitLab CI, or whatever you run), so every push and pull request is verified without anyone remembering to. We stage it for speed: the fast unit and integration tests gate every commit, the slower end-to-end layer runs where it makes sense, and results come back as a clear signal on what broke and where, fast enough that people act on them rather than context-switch away.

  • Test data management and isolation

    The quiet killer of automated suites is shared, mutable test data. A common database that drifts between runs, so tests pass or fail depending on what ran before them. We build a deliberate strategy instead: factories and fixtures that create exactly the data a test needs, seeding and teardown that leave the environment clean, and isolation so tests can run in any order, and in parallel, and give the same answer every time. Deterministic data is most of what makes a suite deterministic.

Where it fits

  • Building a regression suite for a product that ships often

    A team releasing weekly or daily, where manually re-checking the core journeys before each release has become the bottleneck. We build the pyramid around their critical paths (sign-up, checkout, the main workflow), with the bulk of the coverage in fast lower-level tests and a thin end-to-end layer over the few journeys that must survive every release, all running in CI so the manual regression pass shrinks from days to a green build.

  • Rescuing a slow, flaky suite the team has stopped trusting

    An existing suite (often an ice-cream cone of hundreds of slow, brittle end-to-end tests), that fails at random and gets ignored. We diagnose why: fragile selectors, timing races papered over with sleeps, shared test data, and coverage in the wrong layer. Then we stabilise the tests worth keeping, push coverage down the pyramid where it belongs, quarantine and fix the flaky ones, and hand back a suite fast and reliable enough that a red build means something again.

  • Adding a test safety net before a risky change

    A team about to refactor, migrate a framework, or re-platform, with little existing test coverage to catch what the change breaks. We build characterisation and end-to-end tests around the current behaviour first (pinning down what the system does today), so the change can proceed with a net underneath it, and regressions surface in CI rather than in production after the cutover.

  • Standing up automation and CI together for a maturing team

    A team that has grown past manual-only testing and is putting CI/CD in place. We build the automated suite and wire it into the pipeline as one piece of work, staging tests for fast feedback, setting the flakiness and quarantine policy, and leaving the team with both a suite and the practice of keeping it green, so automation becomes part of how they ship rather than a project that decays after handover.

How we approach Automation Testing

We start by deciding what deserves to be automated, because automating the wrong things is how test suites turn into liabilities. Automation earns its keep on checks that are run over and over (the regression suite you would otherwise re-run by hand before every release), and on stable, well-defined behaviour. It is a poor investment on a screen whose design changes weekly, on a one-off verification you will never repeat, or on exploratory work whose whole value is a human noticing something unexpected. So the first deliverable is often a map of your critical journeys and a candid recommendation of which to automate now, which to leave to manual testing, and which are not stable enough to automate yet.

From there we build bottom-up and thin-at-the-top. Logic goes into fast unit tests; the seams to the database, the message queue and the payment provider go into focused integration tests; and only the handful of journeys that genuinely must survive every release (sign up, check out, the core workflow your customers pay for), get an end-to-end test. Each test is written to be deterministic: stable selectors, no sleeping-and-hoping for timing, isolated data, and no dependence on the test that ran before it. A flaky test is treated as a defect in the test, not something to paper over with a retry, because a suite that cries wolf is worse than no suite at all.

How the engagement runs

We open by mapping what matters and deciding what to automate. That means identifying your critical user journeys, the business logic that carries real risk, and the integration seams that break quietly, and being candid about which of those are worth automating now, which belong in manual or exploratory testing, and which are too unstable to automate yet. This is where the money is either well spent or wasted: automating the right, repetitive, stable checks pays off for years, while automating a weekly-changing UI or a one-off verification manufactures maintenance cost and flakiness. You get a clear plan for the shape of the suite before we write a test.

Then we build the suite bottom-up and integrate it into CI early, so it is running against real changes from the first week rather than accumulating on a branch. We put logic into fast unit tests, seams into focused integration and API tests, and only the few must-not-break journeys into end-to-end tests: each one deterministic, isolated, and built on the Page Object Model and stable selectors so it survives the app changing. As the suite grows we keep it fast and green, treat any flakiness as a defect to fix rather than a retry to add, and hand over not just the tests but the practice: how to add to the suite, how to keep it trustworthy, and how to read what it tells you.

The test pyramid and CI integration

The organising model is the test pyramid, and getting its proportions right is most of what separates a suite that helps from one that hurts. The broad base is unit tests (fast, isolated, running in milliseconds), because they are cheap to write, cheap to run, and pinpoint exactly what broke. The middle is integration tests, fewer in number, verifying that your code works correctly against its real collaborators: the database, the queue, the third-party API. The top is a thin layer of end-to-end tests that drive the whole system through the browser to prove the handful of journeys that absolutely must work. Each layer up is slower, more brittle and more expensive to maintain than the one below, so each layer should have fewer tests, and every check should live at the lowest layer that can actually prove it.

The anti-pattern to avoid is the inversion of this. The ice-cream cone: a huge, top-heavy layer of slow, flaky end-to-end tests, a thin sliver of integration tests, and almost no unit tests. It is a seductive trap, because end-to-end tests feel like they prove the most, so teams keep reaching for them. But the result is a suite that takes an hour to run, fails intermittently for reasons unrelated to any real bug, and is so painful to maintain that people start ignoring it or deleting tests to make the pain stop. A suite the team ignores is worse than no suite, because you paid to build it and you are paying to run it. We build the pyramid the right way up precisely to avoid that fate.

None of it delivers value until it runs automatically, so CI integration is part of the architecture, not an afterthought. The suite is wired into your pipeline to run on every push and pull request, staged for fast feedback: the quick unit and integration tests gate every commit and come back in a minute or two, while the slower end-to-end layer runs where it earns its place, on the main branch, before a release, or in parallel to keep the wall-clock time down. The signal has to be fast and clear enough that engineers act on it in the moment, because a test that reports a failure an hour after the developer has moved on is a test that mostly gets ignored.

Test data, secrets and security checks in the pipeline

Automated tests need data and credentials to run, and handled carelessly that becomes its own exposure. We keep secrets out of the test code and out of the repository (no API keys or passwords committed alongside the tests), and inject them at run time from the CI system’s secret store, scoped to test environments rather than production. Test data is synthetic and generated deliberately by factories and fixtures; we do not seed test databases with copies of real customer data, because a test environment is rarely locked down to the standard production data deserves, and a convenience today becomes a breach tomorrow.

The automated pipeline is also the natural place for the security checks that should run on every change, and we wire them in where they fit: dependency scanning to catch known-vulnerable libraries as they are introduced, secret scanning to stop credentials being committed in the first place, and static analysis for the common code-level flaws. These are fast, deterministic checks that belong next to the tests, gating the same pull request. What automation does not replace is genuine security testing. The authorised, human-led work of finding the flaws that chain together into a real compromise; that is a separate discipline, and the automated scans in CI are the cheap first line, not the whole of it.

Signs it’s time

  • You release frequently (several times a week, or continuously), and manually regression-testing before each release has become the thing that slows you down
  • The regression burden is growing faster than the team: every new feature adds more to check by hand, and things that used to work quietly break because nobody re-tested them
  • You have CI in place, or are maturing towards it, and want every commit and pull request verified automatically rather than relying on someone remembering to test the right paths
  • You already have some automated tests but nobody trusts them. They are slow, they fail at random, and the team has started ignoring red builds, which is worse than having no tests at all

Our working method

The principle that governs everything is that a test suite is only worth building if the team will trust it, and trust is fragile. A single test that fails at random teaches everyone that red does not necessarily mean broken, and once that lesson is learned the whole suite loses its meaning. So we optimise relentlessly for determinism and speed: tests that are isolated from each other, that do not depend on shared mutable data, that wait for the application rather than sleeping for a fixed guess, and that run fast enough to fit into the flow of work. Flakiness is treated as a defect in the test to be diagnosed and fixed, never as noise to be silenced with an automatic retry that hides a real problem.

The second principle is honesty about what automation is for. It excels at repetitive regression checks over stable behaviour, and it is a real, ongoing investment: code that has to be maintained as the product changes, not a one-off cost. So we push back on automating the things that waste that investment: unstable UIs that change weekly, one-off checks you will never repeat, and the exploratory testing whose entire value is a human noticing what nobody thought to specify. Automation complements manual and exploratory testing; it does not replace them. Our job is to automate exactly the checks that pay off and to be straight with you about where a person, not a script, is the right tool.

Technologies we build it with

Chosen per problem, not per fashion. This is the stack we most often reach for on this work.

How we deliver

  1. 01

    Discover

    We map the system, the constraints and the business it serves, including the parts nobody documented.

    Architecture brief

  2. 02

    Architect

    Decisions get made, written down and defended before a line of production code exists.

    Decision records

  3. 03

    Build

    Short cycles against working software. You see progress in the product, not in a status deck.

    Shipping increments

  4. 04

    Operate

    Monitoring, incident response and iteration. The system is alive, so the engagement is too.

    Runbooks & SLOs

Want a straight answer on Automation Testing?

A short call with a senior engineer, before you write a brief. If Automation Testing is the wrong answer for your situation, we will say so and tell you what we think is right.

Book a consultationTalk to us

What changes

  • A suite you actually trust

    Deterministic, fast tests where green means the change is safe and red means something real broke, so the team stops ignoring the build and starts relying on it.

  • Regression caught automatically

    Every push verified in CI against the journeys and logic that matter, so the bugs that used to surface in production get caught before they merge.

  • Releases that stop waiting on QA

    A fast automated regression pass on every change means shipping is gated by minutes of pipeline, not days of manual re-testing.

Industries we serve

Domain knowledge changes what gets built. A few of the sectors we know before the first meeting.

How pricing works

  • Fixed-scope build for a defined suite, automating an agreed set of critical journeys and the supporting layers, wired into your CI, quoted once we have mapped the journeys and agreed what is in and out of automation, so you are not paying to automate things that should stay manual.
  • Monthly senior engagement where the suite grows and is maintained alongside the product, with continuity of the people who built it. The right shape when testing is an ongoing part of how you ship rather than a one-off deliverable.
  • Focused rescue or audit of an existing suite, diagnosing why it is slow and flaky, rebalancing the pyramid, stabilising the tests worth keeping and fixing the CI integration, priced by the assessment.

Typical timeline

  1. 01

    Journey mapping and automation plan

    Around a week identifying the critical journeys, the high-risk logic and the integration seams, and agreeing candidly what to automate now, what to leave to manual and exploratory testing, and the shape the pyramid should take.

  2. 02

    Foundation and first tests in CI

    One to two weeks standing up the test framework, the Page Object structure, the test data strategy and the CI wiring, with the first real unit, integration and end-to-end tests running automatically on every push.

  3. 03

    Building out the pyramid

    Iterative coverage of the mapped journeys and logic. The broad base of fast tests first, the thin end-to-end layer over the must-not-break paths. Each test deterministic and maintainable, the suite kept fast and green as it grows.

  4. 04

    Stabilisation and handover

    A flakiness and quarantine policy in place, the suite tuned for speed and reliability, and a handover so your team can add tests, keep the suite trustworthy, and read what it tells them without depending on us.

What working with us actually means

  • Senior engineers who have felt the ice-cream cone

    Test automation is easy to start and easy to get wrong. The top-heavy pyramid, the flaky selectors, the shared data that makes every run a gamble. The people building your suite have maintained automated tests in anger and know which shortcuts turn into a suite the team deletes in a year. No juniors recording brittle clicks and calling it coverage.

  • We operate what we build

    Because we run the systems we ship, we build tests for the long maintenance life they actually have, not for a green tick on day one. That is why the Page Object Model, stable selectors, deterministic data and a flakiness policy are there from the start. They are what keep a suite alive once the codebase starts moving underneath it.

  • Honest about what to automate

    Automation is an investment that pays off on repetitive regression testing and frequent releases, and wastes money on unstable UIs and one-off checks. We tell you which is which before you spend on it, and we are clear that automation complements manual and exploratory testing rather than replacing them.

  • Modern tooling, chosen per layer

    Playwright or Cypress for the browser, Vitest, Jest or pytest for the base, API-level tests where an HTTP call is enough, chosen on your stack, not on habit. We will give you a straight answer on where Selenium still earns its place and where a newer tool wins for a new suite.

How to engage us

Three ways to work with us on this, chosen to fit the problem, not our margin.

Related services

Part of Custom Software Development. Other work we do alongside this.

Common questions

Should we use Playwright, Cypress or Selenium?

For most new suites, Playwright or Cypress. Both were built for modern web apps and auto-wait for the application to be ready, which removes the biggest source of the flakiness that plagued older browser automation. Selenium remains relevant: it has the broadest language and browser support and a huge ecosystem, and if you already have a substantial Selenium suite there is often no case for rewriting it, but for a fresh suite we usually reach for Playwright or Cypress first. We choose on your stack and constraints; our Selenium technology page goes deeper into where it still fits.

How is this different from your manual and exploratory testing services?

They are complementary, not alternatives. Automated tests are excellent at repetitive regression: checking, fast and cheaply, that the things that worked yesterday still work today, but they only ever verify what you told them to check. They do not explore, and they do not notice the unexpected. Manual and exploratory testing is where a human probes new features, follows a hunch, and finds the problems nobody thought to specify. The right strategy uses automation to cover the repetitive regression load so your people are free for the exploratory work only they can do. Our manual testing service covers that other half.

We already have automated tests but nobody trusts them, can you fix that?

Usually, yes, and it is common work. A distrusted suite is almost always some combination of the pyramid being upside down (too many slow, flaky end-to-end tests), fragile selectors, timing races papered over with fixed sleeps, and shared test data that makes runs depend on each other. We diagnose the specific causes, stabilise the tests worth keeping, push coverage down to the layers where it belongs, quarantine and root-cause the flaky ones rather than hiding them behind retries, and fix the CI integration. The goal is a suite where red reliably means something, because that is the only kind worth having.

Is automated testing worth the investment for us?

It depends on how often you release and how repetitive your regression testing is. Automation pays off when the same checks are run over and over (before every release, on every change), and when the behaviour being tested is stable. If you ship frequently and your manual regression pass has become a bottleneck, the investment usually returns quickly. If you release rarely, or the thing you want to test changes every week, automation can cost more to maintain than it saves. We will tell you honestly which situation you are in, and which parts are worth automating rather than automating everything on principle.

How do you stop the tests from becoming flaky and slow over time?

By building for it from the start and treating flakiness as a defect, not noise. Concretely: the Page Object Model and stable, test-dedicated selectors so a UI change is a one-line fix rather than a hundred; tests that wait for the application rather than sleeping for a fixed time; isolated, factory-generated test data so tests do not depend on each other or on a shared database; and the bulk of coverage kept in fast lower-level tests with only a thin end-to-end layer on top. When a test does flake, we diagnose and fix the cause rather than adding a retry that hides it. A suite that stays fast and reliable is a suite the team keeps trusting, which is the entire point.

Thinking about Automation Testing?

Tell us the problem in your own words, not in requirements. A senior engineer reads it and comes back with a straight view on whether Automation Testing is the right answer here, or what would be.

  1. 01A senior engineer reads it. Not a form queue, and not an account manager.
  2. 02We reply either with questions or with a straight answer that we are not the right fit.
  3. 03If it looks like a fit, a technical call with the person who would actually run the delivery.
  4. 04Then scope, effort and risk in writing, before anyone signs anything.
Or book a free consultationPick a time that suits you. No pitch, no obligation.Prefer email? support@yarqat.com

Two fields required. We reply to real enquiries. No list, no sequence.