Software Quality Assurance Services
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
Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.
Trusted by
The brief
Start with what is not working.
Good software decisions begin with the constraint, not a list of features or a preferred technology.
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?
Plain answer
RaftLabs builds automated QA infrastructure for software teams across the US, UK, Europe, Canada, and the UAE: Playwright regression suites, API contract testing, k6 load tests, and mobile QA on real devices. A defect caught in CI costs up to 15x less to fix than one found in production. Fixed price, scoped upfront.
What to remember
- A focused regression suite for one critical flow is fixed scope; full multi-suite coverage is scoped separately. Scoped and fixed before any test code is written.
- QA-as-a-service retainers are monthly, scoped to release cadence and coverage targets.
Proof
- cheaper to fix a defect in development than in production
- Up to 15x
- IBM Systems Sciences Institute, cited by NIST
- average client rating across delivered projects
- 4.9/5
- Clutch, verified reviews
- scope and cost agreed in writing before any test development starts
- Fixed price
- Every RaftLabs engagement
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.
According to IBM research cited by NIST, a defect caught during development costs roughly 15 times less to fix than the same defect found in production, and up to 100 times less for critical system failures. For software teams shipping on a regular cadence, the economics of automated testing are straightforward: the pipeline pays for itself the first time it blocks a production incident.
Automated testing moves quality from a gate at the end of the sprint to a continuous property of the codebase. Each commit runs the suite. The pipeline catches regressions before they merge. Your team decides to ship on current test results, not on how much anyone managed to test by hand in the time available. Once the suite is green, application maintenance and support keeps the product and its tests current.
The test pyramid we build to
Most slow, flaky suites are top-heavy: too many end-to-end tests, too few fast unit tests. A browser test that drives the whole stack to check one calculation is expensive to run and quick to break. We build to the proportions that keep a suite fast enough to run on every pull request and stable enough that a red build means a real bug.
- Unit tests (~70%)
- Fast, isolated checks on individual functions. They run in seconds, pin logic down precisely, and catch a regression where it is cheapest to fix. The base of the pyramid carries the most tests for a reason.
- Integration tests (~20%)
- Confirm that modules, services, and the database work together: API contracts, auth flows, data persistence. Slower than unit tests, far more reliable than routing every check through the browser.
- End-to-end tests (~10%)
- Full user journeys through the real UI. The most convincing and the most expensive to run and maintain, so we reserve them for the flows that lose money when they break, not every screen in the app.
Capabilities
What we build
- 01
Automated regression suites
End-to-end test suites for your web application covering critical user journeys: authentication, core business workflows, payment flows, and edge case inputs. Tests are structured for maintainability with stable
data-testidselectors, so they survive CSS refactors and redesigns. Sharded tests run in parallel on every pull request, with screenshots, video, and trace output for failures, and flaky tests are tracked and fixed systematically rather than silently tolerated.- Built with
- Playwright · Cypress · Page Object Model
- 02
API testing
Automated API contract testing that validates response codes, schema, error structure consistency, authentication enforcement, and edge case inputs. Tooling matches your team, and consumer-driven contracts between microservices ensure a provider cannot deploy a change that breaks its consumers. The API spec stays the source of truth, with a CI check keeping documentation and live behaviour synchronised.
- Built with
- OpenAPI 3.0 · Postman/Newman · Supertest · RestAssured · Pact
- 03
Performance and load testing
Performance testing with load tests that simulate your actual traffic patterns rather than an unrealistic sudden spike. We deliver baseline, load, stress, spike, and soak tests, each answering a different question about how your application behaves under pressure. Pass/fail thresholds live in the test script, so a deployment that degrades p95 response time beyond a set margin is blocked, and results are visualised for response times, error rate, and throughput.
- Built with
- k6 · InfluxDB · Grafana
- 04
Mobile testing
Mobile testing on real iOS and Android devices, not just emulators. The device matrix is built from your user analytics, so effort goes to the OS versions your users actually run. Automated UI tests cover launch, auth, push notifications, offline behaviour, and state transitions, and accessibility is verified too: touch target sizes, VoiceOver and TalkBack compatibility, and colour contrast on mobile viewports.
- Built with
- BrowserStack App Automate · AWS Device Farm · Detox · Appium
- 05
Exploratory and manual testing
Structured exploratory testing using session-based test management: each session is chartered, time-boxed, and documented, producing a coverage record your team can read before a release. Charters come from risk analysis, so recently changed and defect-prone features are tested first. Every defect report includes a severity rating, numbered reproduction steps, and a screenshot or recording, and each significant release gets a readiness report with a clear go/no-go recommendation.
- 06
Test management and reporting
Test plan design for teams without a structured testing process: a written strategy covering scope, test types, environments, data management, defect workflow, and entry/exit criteria for each phase. Test cases are grouped by feature and risk area and cross-referenced to requirements so coverage gaps stay visible. Quality metrics are reported weekly, with defect escape rate as the primary KPI, and reviewed with the engineering lead.
- Built with
- TestRail · Zephyr Scale · Jira, Linear
What a QA engagement costs by scope
We scope every engagement before we price it, so you see the number before we write a line of test code. Use this to map the coverage you need to a realistic build. Every engagement is scoped and fixed-price before test code is written.
Scope to effort
| Scope | What it covers | Typical build | Timeline |
|---|---|---|---|
| Smoke and critical path | Login, checkout, and one core workflow. The regressions that page you at 2am. | Fixed scope | Scoped per engagement |
| Full regression suite | All primary user journeys, API contract tests, and CI gating on every pull request. | Fixed scope | Scoped per engagement |
| Performance and load | k6 baseline, load, stress, and soak tests with p95 thresholds wired into the pipeline. | Fixed scope | Scoped per engagement |
| QA-as-a-service | Ongoing maintenance, pre-release exploratory testing, and weekly quality reporting. | Monthly retainer, scoped to release cadence | Continuous |
Most teams start with the smoke-and-critical-path tier to prove the pipeline pays for itself, then expand coverage outward from there. The first shippable slice is one suite guarding your highest-risk flow, not full coverage on day one.
How we work
From scope to shipped
Every QA engagement follows the same four phases. Coverage targets are locked and price is fixed before any test development starts.
- Phase 101
Audit and risk analysis
We review your application, existing test coverage, release cadence, and defect history. You leave the first phase with a written QA strategy document: which areas to test first, which frameworks to use, and a fixed-price quote. No test development starts without your sign-off.
- Phase 202
Test design and infrastructure
Test cases designed before automation code is written. Framework scaffolding, CI/CD integration, and test data strategy established in the second phase. The test architecture is locked before the build starts, preventing rework when coverage expands.
- Phase 303
Build, automate, and validate
Automated suite built in priority order: highest-risk areas first. Each sprint adds coverage and runs existing tests to catch regressions in the suite itself. QA runs in parallel with your development sprints, not as a phase at the end.
- Phase 404
Handover and ongoing support
Suite handed over with full documentation, CI/CD integration active, and a defect reporting workflow in place. QA-as-a-service retainers include ongoing test maintenance, exploratory testing before releases, and weekly quality reporting.
Why us
Why teams choose RaftLabs
- 01
The team that scopes builds
The engineers who assess your QA problem also build the test infrastructure. No bait-and-switch, no handoff after the contract is signed. The team you meet in the first phase ships the final one.
- 02
Fixed price before development starts
We scope the work, calculate the cost, and lock it in writing before any test development starts. A scope change is a change request: priced, agreed, or dropped. It never absorbs into the project and appears on the final invoice.
- 03
Shipping production software since 2015
Real engagements across SaaS, fintech, healthcare, and logistics, not every one published as a named case study. A decade of shipped products, with QA and testing work across SaaS platforms, mobile apps, and enterprise systems in healthcare, FinTech, logistics, and hospitality.
- 04
Compliance scoped from the start
Compliance requirements are scoped in the first phase, not retrofitted before launch. Your legal advisers confirm which regulations apply; we implement what they approve.
Pitfalls we plan around
Most QA programmes fail in predictable ways. We scope around these from the first phase rather than discovering them in production.
- The 100% coverage myth
- Chasing a coverage percentage rewards testing trivial getters and ignores the risky paths. We target the flows that cost money when they break, not a number on a dashboard that looks reassuring and proves little.
- Flaky tests
- A suite that fails at random gets ignored, and an ignored suite catches nothing. We trace flakiness to its source (timing, shared state, unstable test data) and quarantine the offender rather than retrying until it goes green.
- Manual-only regression
- Re-testing the whole application by hand every release does not scale. The cycle grows with the codebase until it becomes the release bottleneck. Automation moves regression off the critical path.
- Testing after the fact
- QA bolted on at the end of a sprint finds bugs when they are most expensive to fix. Tests run on every pull request, so a regression surfaces the day it is written, not the week before launch.
Where QA is heading: AI-assisted test generation
Large language models now draft test cases from a user story, generate edge-case inputs a tired human would skip, and turn a bug report into a failing regression test before the fix lands. We use them to speed up the writing, not to own the suite. The model drafts; an engineer reviews, prunes, and signs off. A test nobody understands is worse than no test, so a human still decides what reaches your pipeline. We build toward this deliberately rather than shipping machine-written tests you cannot maintain.
Proof
Tested under real load
- 01
Political engagement app built for election-scale load
- people connected at the same time in load testing
- ~7,854
- 02
EventRaft reaches 50,000 users in 6 months by replacing scattered event tools with one platform
EventRaft replaces the three to five tools most event organizers juggle with one native iOS and Android platform. 50,000 users in six months, 95% satisfaction, and 25% faster event management (client-reported).- in 6 months
- 50k+ users
- satisfied users
- 95%
- faster event management
- 25%
- 03
Grubly processes 10,000+ restaurant orders while saving owners $5,000 in third-party delivery commissions
Grubly lets restaurants own their online ordering channel. 10,000+ orders processed, $5,000 saved in delivery commissions in the first 15 days, and a direct customer relationship that third-party platforms do not allow.- transactions in first 15 days
- $ 10k+
- savings on food delivery commissions
- $ 5k
- zero to launch
- 12 weeks
Work with us
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.
- Scope and cost agreed before work starts. No surprises. No obligation.
- Working prototype within 3 weeks of kickoff.
- Pay by milestone. You see progress before each invoice.
- 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
- All conversations are NDA-protected.
Common questions
Automated testing runs defined scenarios without human involvement, in minutes, on every code change. It suits regression testing, API contract testing, and performance testing. Manual testing needs a human exercising the application. It suits exploratory testing, usability testing, and new features not yet stable enough to automate. Most teams need both. We build automated suites for stable, well-defined 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.
QA project cost depends on application complexity, the number of test scenarios, frameworks involved, and whether you need ongoing retainer coverage or a one-time suite build. A focused Playwright regression suite for a mid-sized web app is fixed scope, priced after coverage scope is agreed. QA-as-a-service retainers are monthly, scoped to release cadence and coverage targets. Every engagement is scoped and fixed-price before development starts - you see the cost before we write a single line of test code.
An honest answer: we guarantee the quality of the testing against the specifications, not a bug-free product. No testing engagement can promise zero defects: anyone who does is selling certainty they cannot deliver. What you can demand is rigor: coverage targets locked before work starts, tests versioned in source control like code, and close cooperation with your developers through the whole release.
Yes. This is more common than buyers admit. Research and exploratory testing start from the product as it exists: testers extract expected behaviour from your team, build the missing test documentation, and often end up writing the specification you never had. A chaotic, long-built app nobody fully understands is a normal starting point, not a disqualifier.
Ask to see the tests run and ask about flaky tests: how often do tests fail without a real bug, and what is done about it? Brittle tests that fail under load or when layouts change cost more than they save. Require tests to live in your source control, versioned like code, so you own them. And judge the reporting: good reporting shows what was tested, what failed, and what it means for the release, not just a green percentage.
We have delivered QA and testing services for software teams across FinTech (payment flow validation, API contract testing), healthcare (clinical workflow regression), e-commerce (performance testing ahead of peak traffic events), SaaS (regression suites integrated into CI/CD pipelines), and logistics (mobile app testing on devices used by warehouse staff). The testing approach is the same across industries: risk-based, automated-first. Compliance constraints and critical paths differ. Your legal advisers confirm which regulations apply; we factor them in during scoping.