Real Results, Real Startups
Six detailed, anonymised case studies from our most impactful QA engagements across Bengaluru and beyond.
How a Bengaluru Insurtech Startup Reduced Regression Time by 70% and Shipped 3x Faster
!The Challenge
A fast-growing insurtech startup had a 6-hour manual regression suite blocking every release. With 3 production incidents in 2 months and a QA engineer nearing burnout, the team was stuck. Their abandoned Cypress suite had 47 flaky tests and zero developer trust.
→Our Solution
We replaced the flaky Cypress suite with a Playwright + AI framework. Within 8 weeks: 145 stable tests covering all critical insurance flows (policy creation, claim submission, document upload, payment). AI-generated edge cases from Jira tickets added 60 additional tests. Self-healing layer deployed. Full CI/CD integration with parallel execution across 6 workers.
Results Delivered
“"Our engineers actually trust the test suite now. We went from releasing at 2am once a week to shipping confidently 3 times a week."”
— VP Engineering, Insurtech Startup
Sleep-Monitoring HealthTech App Survives 10x Traffic Spike After BTQA Performance Overhaul
!The Challenge
A healthtech startup's sleep-monitoring platform experienced a viral moment when a popular doctor mentioned their app on Twitter. Traffic spiked 10x overnight. The app crashed within 45 minutes. Database connections were exhausted, API response times hit 18 seconds, and 2,000 users left 1-star reviews.
→Our Solution
Post-incident engagement: full load testing with k6 revealing 4 critical bottlenecks (N+1 queries, missing Redis caching, unoptimised sleep data aggregation queries, no CDN for sensor data uploads). We fixed the architecture and implemented a proactive load testing pipeline to catch regressions before they reach production.
Results Delivered
“"The second viral moment came 6 weeks later. This time we didn't crash. BTQAS made us ready."”
— CTO, HealthTech Startup
EdTech Platform Cut Test Maintenance by 95% with AI Self-Healing
!The Challenge
A rapidly growing edtech platform was rebuilding their frontend every sprint (React component library migrations). Their Selenium test suite was breaking 30–40% of tests every single sprint, and the QA team was spending more time fixing tests than writing new ones. Coverage was actually declining as the product grew.
→Our Solution
Migrated the entire suite from Selenium to Playwright with our custom AI self-healing layer. The healing system uses element fingerprinting across 10 attributes, falls back gracefully when primary locators break, and auto-updates selectors after frontend migrations. Rebuilt 200 tests with proper Page Object Model architecture.
Results Delivered
“"We used to dread frontend sprints because of broken tests. Now we barely notice. The self-healing just works."”
— Lead QA Engineer, EdTech Platform
Order-to-Cash SaaS Startup Achieved 100% Critical-Path Coverage in 4 Weeks
!The Challenge
A B2B SaaS startup automating order-to-cash workflows for mid-size enterprises was approaching their first enterprise client onboarding. The client's procurement team required a QA audit and evidence of automated test coverage on all critical financial workflows. The startup had zero automation and 4 weeks to fix it.
→Our Solution
We ran a compressed 4-week engagement: Week 1 — QA audit and risk prioritisation. Weeks 2–3 — automated the 12 critical financial flows (order creation, approval chains, invoice generation, payment reconciliation, exception handling). Week 4 — CI/CD integration, documentation, and client-facing QA report.
Results Delivered
“"We almost lost our first enterprise deal because of our QA gap. BTQAS delivered the entire solution in 4 weeks. The client signed."”
— CEO, B2B SaaS Startup
DeepTech AI Startup Tested Copilot-Generated Code with Zero Defects in Production
!The Challenge
A Bengaluru deeptech startup was using GitHub Copilot and Cursor to generate 60%+ of their backend code. While velocity was high, the engineering team had no systematic way to validate AI-generated code — and discovered 3 silent data corruption bugs during internal testing that could have been catastrophic in production.
→Our Solution
Designed a specialised "AI code confidence testing" framework: property-based testing for AI-generated functions, boundary condition validation, data integrity assertions, and an automated review pipeline that flags AI-generated code for enhanced test coverage. Integrated into every PR via GitHub Actions.
Results Delivered
“"We couldn't slow down our AI-assisted development velocity, but we needed confidence in the output. BTQAS built us a safety net that works invisibly."”
— Head of Engineering, DeepTech Startup
How a Bengaluru Startup Scaled Mobile + Web Testing with One AI Framework
!The Challenge
A consumer fintech app (web + iOS + Android) was maintaining three separate test suites — one for each platform. The combined maintenance cost was 60+ hours per sprint. Tests diverged, coverage was inconsistent across platforms, and the mobile suite was perpetually 2 sprints behind the web suite.
→Our Solution
Replaced the 3 separate suites with a unified Playwright + AI agent framework using shared test logic with platform-specific rendering layers. A single test scenario now executes on web, iOS simulator, and Android emulator from one codebase. AI agents handle platform-specific gesture differences automatically.
Results Delivered
“"One test suite. Three platforms. The same coverage. I didn't think it was possible to achieve this in 6 weeks."”
— Product Lead, Consumer Fintech App
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