What test automation services include

What do test automation services include?

Test automation services cover the full life of automated tests: deciding what to automate, choosing tools, writing the tests, connecting them to your CI/CD pipeline, reporting results and keeping the suite healthy as your product changes. A good automated software testing company spends as much effort on maintenance and reliability as on writing new tests.

Our work usually includes:

• An automation assessment of your product, current tests and release process.
• Framework setup with coding standards, folder structure and reusable helpers.
• Automated UI tests for critical user journeys on web and mobile.
• Automated API tests for business rules, data and integrations.
• Pipeline integration, so tests run on pull requests, nightly and before releases.
• Result dashboards and alerts your team actually reads.
• Ongoing maintenance, including fixing flaky tests and updating tests for new features.

Test automation is one part of a wider QA approach. If you also need manual, exploratory or device testing, see our QA as a Service offering.

What to automate and what to keep manual

What should you automate, and what should stay manual?

Automate checks that are stable, repeated on every release and costly to get wrong. Keep tests manual when they need human judgment, change every sprint or run only once. Trying to automate everything is the most common reason automation projects stall.

Manual testing doesn’t disappear. It moves to where people add the most value. Our outsourced QA testing services cover the manual side when you need both.

Choosing a framework

Which test automation framework is right for you?

The right framework is the one that fits your tech stack, your team’s languages and the platforms you must support. For most new web projects in 2026, Playwright is a strong default. Selenium still suits large Java, Python or C# teams, and Cypress suits JavaScript front-end teams that test mainly in Chromium browsers.

Our test automation services don’t force a tool on you. If your developers write TypeScript, we write tests in TypeScript. If you already have a Selenium suite, we usually stabilize it before suggesting any migration.

Where automated tests should live

Where should automated tests live: UI, API or unit?

Most automated tests should sit below the user interface. The test automation pyramid puts many fast unit tests at the base, a solid layer of API and integration tests in the middle, and a small number of end-to-end UI tests at the top for critical journeys only.

UI tests are slower and break more easily when the layout changes. API tests check the same business rules in a fraction of the time. In practice, our automation testing services focus on two layers:

• API tests for rules, calculations, permissions and integrations.
• A lean set of UI tests for the journeys that make or lose money, such as sign-up, checkout and reporting.

Unit tests usually stay with your developers, because they are written alongside the code. We can review unit test coverage and suggest gaps, but we don’t replace that work.

CI/CD integration

How do automated tests run in your CI/CD pipeline?

An automated testing service is most useful when tests run without anyone pressing a button. We connect your suite to GitHub Actions, GitLab CI, Jenkins or Azure DevOps and set up three run types.

Results post to the places your team already looks, such as the pull request, Slack or Microsoft Teams. Failed tests include screenshots, videos or traces, so developers can see what went wrong without rerunning anything.

Test data and environments

How do you handle test data and environments?

Unreliable test data causes more failed automation runs than bad test code. We plan data and environments before writing large numbers of tests.

• Each test creates its own data through APIs where possible, then cleans it up, so tests don’t depend on each other.
• Masked or synthetic data only, never real customer records. For healthcare products, CloudOryx is not HIPAA certified, so we agree data rules with you in writing.
• Stable test environments with known versions of services, or mocks for third-party systems that are slow or costly to call.
• Secrets kept in your pipeline’s secret store, not in test code.

Test automation ROI: a worked example

What is the ROI of test automation? A worked example

Test automation pays off when the hours it saves on repeated regression testing are greater than the hours spent building and maintaining it. A simple way to estimate the payback period is: build hours ÷ (manual hours saved per month − maintenance hours per month).

Here is an illustrative example. The numbers are assumptions to show the math, not results from a specific client.

Hours are only part of the return on test automation services. Faster feedback means developers fix bugs while the code is still fresh, and releases can go out more often. Teams that release weekly usually see payback sooner than this example.

Flaky tests and maintenance

How do you keep automated tests from becoming flaky?

A flaky test passes and fails on the same code without any real change. Flaky tests are dangerous because teams learn to ignore red builds. In software test automation, we treat flakiness as a defect in the test suite and fix it on a schedule.

• Track the flake rate for every test and review the worst offenders each sprint.
• Quarantine a flaky test so it stops blocking releases, then fix or delete it within an agreed time.
• Use stable selectors, such as test IDs agreed with your developers, instead of fragile page paths.
• Prefer built-in waiting over fixed sleeps, which slow suites down and still fail.
• Review the suite quarterly and remove tests that no longer protect anything important.

After handover, you choose the maintenance model: we keep maintaining the suite as part of a monthly engagement, or we train your developers and step back.

Our 4-phase automation roadmap

What does a test automation roadmap look like?

Every test automation services engagement we run follows four phases, so you see working tests early and can adjust before investing more.

1. Assess (about 1 to 2 weeks): review the product, current tests and pipeline; agree the first flows to automate and success metrics.
2. Foundation (about 2 to 4 weeks): set up the framework, coding standards, test data approach and pipeline runs; automate the first critical journeys.
3. Scale (ongoing): add API and UI coverage sprint by sprint, starting with the highest-risk areas.
4. Optimize (ongoing): speed up runs with parallel execution, cut flaky tests and retire low-value tests.

Timelines depend on product size and how testable the app is, so we confirm them after the assessment phase.

AI-assisted test automation

Can AI help with test automation?

Yes, with limits. AI tools can draft test cases from requirements, suggest test data and help repair selectors when the UI changes. A human engineer still decides what to test, reviews every generated test and owns the result. We use AI to speed up routine work, not to replace test design.

Our practical guide to AI-driven software testing explains where AI helps and where it doesn’t. If you are testing AI features themselves, read our QA playbook for LLM apps.

Our QA work is led by Mirza Asif, who has built QA as a Service programs and AI-assisted QA frameworks. See an example of our QA work in the Logix9 case study.