How many production incidents in the last three months could have been caught by a regression test that did not exist?
When a release deadline moves up by a week, what gets cut -- and how often is it testing?
Bugs in production cost more than bugs caught before deployment. Most QA processes are designed to catch them after.
Manual testing cycles take days, block releases, and still miss the edge cases that break in production. When QA is the last thing cut to meet a deadline, it is the first place bugs escape. When testing only happens before release, regressions introduced mid-sprint go undetected until someone reports them.
We build automated testing infrastructure and provide QA-as-a-service for software teams that need consistent quality without a full-time internal QA headcount. Test automation, regression suites, performance testing, API testing, and mobile testing. Quality as a continuous property of the codebase, not a gate at the end of the sprint.
Automated regression suites that run on every deployment and catch breaking changes before they reach production
API testing that validates contract behaviour, error handling, and edge cases your manual testers miss
Performance testing that identifies response time degradation before it becomes a user complaint
Mobile testing across real devices, not just emulators, for iOS and Android applications
RaftLabs provides quality assurance and testing services including automated regression suite development using Playwright, Cypress, or Selenium, API testing with Postman or RestAssured, performance and load testing using k6 or JMeter, mobile testing for iOS and Android on real devices, security testing for common vulnerability patterns, test management and defect tracking setup, and QA process design for teams without structured testing practices. QA engagements are scoped at a fixed price based on application complexity, test coverage targets, and integration with your existing CI/CD pipeline.
Quality is a continuous property, not an audit gate
A software team that tests only before release has a testing problem disguised as a release problem. Regressions accumulate between releases. Edge cases appear in production that nobody tested for. The manual testing cycle takes longer as the application grows, until it becomes the constraint on release velocity.
Automated testing moves quality from a gate at the end of the sprint to a continuous property of the codebase. Every change is tested. Regressions are caught in the pipeline before they merge. Release decisions are based on current test results, not on how much the team managed to test manually in the time available.
What we build
Automated regression suites
End-to-end test suites for your web application using Playwright or Cypress. Test scenarios covering critical user journeys: authentication, core business workflows, payment flows, and data entry. Tests structured for maintainability: page object model, reusable helper functions, clear test naming. CI/CD integration so the suite runs automatically on every pull request. Failure reporting with screenshots and videos of failing tests so your team can debug without reproducing manually. A regression safety net that grows as your application grows.
API testing
Automated API contract testing that validates endpoint behaviour: correct response codes, response schema validation, error handling, authentication enforcement, and edge case inputs. Postman collections with Newman for CI/CD integration, or code-based API tests using Supertest or RestAssured. API documentation generated from test specifications so contract testing and documentation stay in sync. Contract testing for microservices that consume each other's APIs: detecting breaking changes before they cause production failures between services.
Performance and load testing
Performance testing that measures how your application behaves under load before your users find out. Load tests using k6 that simulate realistic concurrent user behaviour from your actual usage patterns. Baseline performance measurement and threshold alerting: if a deployment degrades response time by more than a defined percentage, the test fails. Stress tests that identify the breaking point -- the load level at which response times degrade unacceptably or the application starts returning errors. Performance test results integrated into your CI/CD pipeline for scheduled pre-release runs. The data to make infrastructure scaling decisions from evidence rather than guesswork.
Mobile testing
Mobile application testing on real iOS and Android devices using BrowserStack or AWS Device Farm. Cross-device and cross-OS-version test runs that catch device-specific rendering and behaviour issues. Automated UI tests for React Native and native mobile applications. Network condition simulation (3G, limited connectivity) to validate application behaviour on poor connections. Accessibility checks for mobile: touch target sizes, screen reader compatibility, contrast ratios on mobile viewports. The coverage that catches the device-specific bugs that emulators do not reproduce.
Exploratory and manual testing
Structured manual testing sessions by experienced QA engineers for new features and release sign-off. Session-based exploratory testing with documented coverage and findings. Usability observation: testing from the perspective of a user encountering the interface for the first time and documenting friction points. Defect documentation with reproduction steps, screenshots, and severity ratings that give developers everything they need to reproduce and fix without back-and-forth. Release readiness reports that summarise test coverage, open defects by severity, and known risks before a production release.
Test management and reporting
Test plan design and management for teams without a structured testing process. Test case library organisation using TestRail, Zephyr, or Notion depending on your team's existing tools. Defect tracking workflow integration with Jira or Linear. Test coverage metrics and trend reporting: how many test cases exist, what percentage are automated, defect discovery rate by test type, and defect escape rate (bugs found in production versus pre-production). The visibility into your quality process that moves quality decisions from intuition to data.
What would it take for your team to deploy with confidence on a Friday?
Tell us your current release process and where quality risks sit. We will scope the test automation infrastructure that closes them.
Related services
DevOps as a Service -- CI/CD pipelines that run your automated test suite on every deployment
Product Engineering -- full-stack development with QA built into the delivery process
Software Modernisation -- modernising legacy applications with test coverage added progressively
API Development -- API development with contract testing from the first endpoint
Custom Software Development -- end-to-end software delivery with quality assurance included
Frequently asked questions
Automated testing executes a defined set of test scenarios without human involvement, runs in seconds or minutes rather than hours, and can run on every code change through a CI/CD pipeline. It is the right approach for regression testing (confirming existing features still work after changes), API contract testing (validating endpoint behaviour and response structure), and performance testing (simulating load to measure response time degradation). Manual testing requires a human to exercise the application and observe behaviour. It is the right approach for exploratory testing (finding unexpected problems a scripted test would not look for), usability testing (evaluating whether the interface is intuitive), and new feature testing before the feature is stable enough to write reliable automation against. Most software teams need both. The ratio depends on the maturity of your codebase and the stability of your test targets. We build automated suites for stable, well-defined test scenarios and recommend manual testing for exploratory and new feature work.
For browser-based end-to-end testing, Playwright is the current default for new projects. It is faster and more reliable than Selenium, supports all major browsers natively, has excellent async/await API design, and has built-in support for mobile viewports and network interception. Cypress is a strong alternative with better developer tooling and a more accessible learning curve, but is limited to Chromium-based browsers for cross-browser testing. Selenium remains relevant for teams with existing Selenium infrastructure or specific browser coverage requirements. For API testing, Postman and Newman for collection-based API testing, or RestAssured for Java projects or Supertest for Node.js. For performance testing, k6 is the modern choice: JavaScript scripting, CI/CD integration, and both open source and cloud-hosted options. JMeter is the legacy choice with a larger existing install base. We recommend based on your technology stack, team expertise, and testing requirements.
QA-as-a-service means RaftLabs acts as your QA capability rather than your team hiring and managing QA engineers internally. We scope, design, build, and maintain your automated test suite. We run manual exploratory testing before releases. We triage and document defects. We report on test coverage, defect trends, and release readiness. For teams that do not have enough consistent QA work to justify a full-time hire, or that are moving too fast to train and manage internal QA, a retainer model with RaftLabs delivers consistent QA coverage without the overhead. The scope of each retainer is defined based on release cadence, application complexity, and test coverage targets.
Legacy applications with no test coverage are the most common starting point. We do not try to write tests for everything at once -- that approach fails because the test suite takes too long to build and provides too little value too slowly. We use a risk-based approach: identify the highest-risk areas of the application (features that generate the most support tickets, payment flows, authentication, data import/export) and build test coverage there first. As the automated suite grows, we add coverage for lower-risk areas progressively. For applications with no API documentation, we document the API contracts as we write tests for them, which is a useful deliverable in itself. We set a realistic test coverage target and timeline during scoping rather than promising full coverage immediately.