Quality Engineering Services
Quality engineered in.
Not tested in at the end.
Defects caught at commit cost a fraction of defects caught in production. We build AI-powered test automation, performance, security, and accessibility validation into every stage of your delivery pipeline so your team ships with confidence.
Our Decade long experience, validated in numbers
Enterprise Customers
What shifts when quality is built in
Three things your team stops
Worrying about.
Regressions reaching production
Automated regression runs on every commit. Broken builds surface in minutes, not after a release cycle. Your team merges with confidence.
Performance surprises under load
Throughput and latency baselines are validated in the pipeline. Traffic spikes stop being surprises you've already tested against them with k6, JMeter, and Gatling.
Security findings after launch
SAST, DAST, and dependency scanning run continuously. Vulnerabilities are caught at code level, not in a quarterly pen-test report.
Trusted by Leading Enterprises
Testing capabilities at a glance
A bird's-eye view of
What quality engineering covers.
Five testing disciplines, one unified strategy from a single commit to production.
Functional & Regression
Playwright · Cypress · Selenium
WebdriverIO · TestCafe
Cucumber · SpecFlow · pytest
Cross-browser · BDD · E2E
API & Integration
Postman · RestAssured · Karate
SoapUI · Pact · WireMock
Newman · Hoverfly
Contract · Schema · Events
Performance Engineering
k6 · JMeter · Gatling
Locust · Artillery · Taurus
Lighthouse · BlazeMeter
Security Testing
OWASP ZAP · Burp Suite
Snyk · SonarQube · Checkmarx
SAST · DAST · SCA
AI TEST
INTELLIGENCE
Mobile & Accessibility
Appium · Espresso · BrowserStack
Axe · Percy · Applitools · Pa11y
Capabilities
Five disciplines. One quality strategy.
Functional & regression testing
Comprehensive coverage of user flows and business rules, running on every commit. Playwright leads our cross-browser automation suite supporting Chromium, Firefox, and WebKit with a single API.
- Playwright, Cypress, and Selenium for cross-browser E2E automation
- BDD with Cucumber and SpecFlow tests your business team can read
- Data-driven frameworks covering complex rule matrices
- Parallel execution with Allure and ExtentReports dashboards
- Integrated into GitHub Actions, GitLab CI, Jenkins, Azure DevOps
API & integration testing
Microservices fail at the seams. We validate contracts, schemas, and data integrity between services before they’re wired together catching integration drift early.
- REST, GraphQL, gRPC, and SOAP automation and validation
- Consumer-driven contract testing with Pact
- Schema validation and API backward-compatibility checks
- Service virtualization for testing without downstream dependencies
- Event-driven testing for Kafka, RabbitMQ, and SQS
Performance engineering
Performance isn’t a feature you add before launch. We set baselines early, validate against real traffic profiles, and wire alerting into the pipeline so degradation is caught before users feel it.
- Load, stress, soak, spike, and scalability testing
- p95 / p99 SLA validation against real-world user profiles
- Core Web Vitals, FCP, LCP, CLS, and TTFB profiling
- Continuous performance gates in CI/CD threshold-based build failures
- Distributed cloud load generation at scale
Security testing & compliance
A security test at the end of a release cycle is an audit. We shift security left scanning code, dependencies, containers, and running applications continuously.
- SAST: static analysis across source code and infrastructure-as-code
- DAST: runtime scanning for OWASP Top 10 and API vulnerabilities
- SCA: dependency and open-source license compliance
- Secrets detection and container image scanning
- GDPR, HIPAA, and SOC 2 control mapping patterns
Mobile & accessibility testing
Mobile and accessibility are still afterthoughts in many delivery cycles. We fold them into the same automation discipline applied everywhere else real devices, real standards.
- Native and hybrid mobile automation on iOS and Android
- Real device and emulator testing at scale via cloud farms
- WCAG 2.1 / 2.2 automated audits and remediation guidance
- Visual AI regression pixel differences that matter, not rendering noise
- Gesture, biometric, and network condition simulation
Our approach
How we think about building this.
Autonomous test generation
LLM-powered models generate test cases from user stories, API schemas, and code diffs cutting the time from feature to coverage.
Self-healing selectors
When UI elements change, AI-driven locator strategies self-repair. Maintenance overhead the main reason automation suites are abandoned drops significantly.
Predictive test selection
ML models trained on your defect history select the tests most likely to catch failures for any given commit the full suite runs only when warranted.
Visual AI comparison
AI-based visual testing understands what constitutes a real regression versus a rendering difference fewer false positives, more genuine signal across browsers and viewports.
Flake detection & root cause
AI analysis of test history identifies flaky tests and common failure patterns converting noise into actionable signal your team can close out.
Coverage gap analysis
Static and runtime analysis mapped against AI risk profiles surfaces untested code paths prioritised by actual change frequency and defect proximity.
Platforms & tooling
Built on what your team already trusts.
UI / Functional Automation
BDD / TDD Frameworks
API & Integration Testing
Performance & Load
Security - SAST / DAST / SCA
Mobile Testing
Visual & Accessibility
AI-Augmented Testing
Test Management & Reporting
CI/CD Integration
Everything you need to know
Quality engineering builds testing, including functional, performance, security, and accessibility validation, directly into every stage of the delivery pipeline, rather than treating quality as a separate phase that happens after development is complete. Traditional QA typically tests a build after it's done; quality engineering catches defects at commit, which costs a fraction of catching the same defect once it reaches production.
Self-healing test automation uses AI-driven locator strategies to automatically repair test selectors when UI elements change, instead of a test failing and requiring a developer to manually update it. This matters because ongoing maintenance overhead is the main reason automated test suites get abandoned over time, so reducing that overhead is what keeps a test suite useful months or years after it's first written.
Predictive test selection uses machine learning models trained on an application's defect history to identify which tests are most likely to catch a failure for a given code change, so the full test suite only runs when it's actually warranted. This is different from running every test on every commit regardless of what changed, which becomes slow and expensive as a test suite grows.
SAST (static application security testing) analyzes source code for vulnerabilities without running the application, DAST (dynamic application security testing) tests a running application by simulating attacks against it, and SCA (software composition analysis) scans third-party dependencies for known vulnerabilities. Running all three continuously in the pipeline catches vulnerabilities at the code and dependency level, rather than relying on a periodic penetration test after the application is already in production.
AI-powered test generation uses large language models to create test cases directly from user stories, API schemas, and code diffs, reducing the time between a feature being built and it having test coverage. This is paired with visual AI comparison, which distinguishes a genuine visual regression from a harmless rendering difference between browsers, reducing false positives that would otherwise require manual review.
Yes, performance engineering validates throughput and latency baselines directly in the CI/CD pipeline using tools like k6, JMeter, and Gatling, so traffic spikes and load-related issues are tested against before a release ships rather than discovered during a real spike. This turns performance from a reactive incident response problem into something checked automatically on every relevant change.
Where these capabilities apply
Edge-to-Cloud AI across our platforms and services.
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