How we've helped companies improve their testing processes and deliver quality software
Reducing regression testing time by 70% for critical financial transactions
A leading investment bank needed to test their SWIFT payment processing system that handles thousands of high-value transactions daily. Manual testing was taking 5 days per release cycle, causing deployment delays and increased risk of production bugs.
Implemented a comprehensive test automation framework using Selenium WebDriver with Java, integrated with their CI/CD pipeline.
Page Object Model with data-driven approach for testing multiple SWIFT message types
RestAssured for backend validation and database checks
Jenkins integration with automated test execution on every commit
Real-time test results with Allure Reports and Slack notifications
From 5 days to 1.5 days per cycle
Covering critical payment flows
End-to-end transaction flows
In 6 months post-implementation
"The automation framework has transformed our testing process. We now deploy with confidence and catch issues much earlier in the development cycle."
Preventing major failures during Black Friday launch
An e-commerce startup was preparing to launch their platform during the critical Black Friday period. They needed comprehensive testing to ensure the site could handle high traffic and transactions without failures.
Executed a comprehensive go-live QA strategy focusing on performance, functionality, and user experience.
End-to-end testing of user flows, checkout, payment processing
JMeter load tests simulating 150,000 concurrent users
Payment security, SQL injection, XSS vulnerability checks
iOS/Android app testing across 15 devices
Fixed before launch
During Black Friday weekend
Launch weekend sales
User satisfaction
"Swapnil's thorough testing saved our launch. The bugs he found would have caused major issues on our biggest sales day."
Ensuring accuracy and reliability of AI medical recommendations
A healthcare tech company built an AI platform to assist doctors with diagnosis recommendations. The ML model needed rigorous testing to ensure accuracy and prevent harmful false positives/negatives.
Developed specialized AI testing strategies focusing on model validation, bias detection, and edge case scenarios.
1000+ test cases covering various medical conditions and patient profiles
Testing for demographic biases (age, gender, ethnicity)
Rare conditions, conflicting symptoms, missing data handling
HIPAA compliance validation and data privacy testing
Above industry standard
That would cause failures
Zero security violations
Successful rollout
"The AI testing approach caught issues our data scientists missed. Critical for healthcare applications where accuracy is life-or-death."
Let's discuss how comprehensive QA testing can improve your software quality and reduce risks