Background
Reservamos SaaS is a technology partner for bus companies in the world. Its AI-powered eCommerce solution adapts to each company’s web and mobile platforms, enhancing conversion rates, streamlining the purchase funnel, and providing a secure, personalized user experience from search to ticket generation.
Problem - One App, Hundreds of Variations, Deployed to Multiple Customers
Because Reservamos’s software requires multi-tenant deployment, keeping quality high and shipping bug-free code is an existential, costly imperative. Not only does Reservamos SaaS need to test the full functionalities of its technology adapted to each partner’s online sales channels, but it must also support variations among customers that require testing of the same change across multiple deployments.
The company’s fast growth raised the stakes of delivering continuous innovation for all of its partners, at high velocity and with reliable quality. This, in turn, required the ability to see, control, and resolve errors across multiple deployments and a rapidly growing base of third-party dependencies.
Finally, Reservamos SaaS is a B2B2C purchasing experience, which means that every error in the checkout flow results in lost revenue for customers. Lost revenue for its customers means revenue risk for Reservamos. The challenge wasn’t just an issue for the engineering team, but a P&L urgency for the entire company.
Constant Maintenance makes QA Automation a $200K+ Costly Endeavor
Reservamos’s rapid innovation and continuous deployment across clients’ digital channels make writing end-to-end tests a costly, never-ending task.
The real-time nature of the data flowing through Reservamos SaaS meant there was no consistent state. Reservamos SaaS wanted to test their eCommerce technology with real market data, which meant that tests were often flaky as the data, prices, and availability flickered on a nearly minute-to-minute basis.
Overall, Reservamos SaaS engineers were spending 20% of their time on quality issues and needed three QA automation engineers just to maintain their tests – a significant engineering
commitment with unacceptably high costs.
Results - Full testing suite across all environments, automatically maintained, with automated PR updates by Checksum.
Using AI, Checksum worked with Reservamos SaaS to generate a full testing suite across all environments within one month. The tests are automatically updated in real-time so as third-party changes go online, Checksum tests immediately respond, sending a PR to the codebase. The Reservamos team reviews and makes minimal changes as necessary.
Checksum’s team tracks every test, monitors performance, and ensures the Reservamos SaaS team operates from a complete, always-on, fully functional testing suite. Instead of continually fixing bugs in the last release, the Reservamos SaaS team can now devote its energies to building tomorrow’s capabilities today.
What would have cost the Reservamos SaaS team $200K in full-time salaries —and that’s hard, direct costs, not opportunity costs of time spent firefighting — is now delivered by Checksum at a significant Return on Investment (ROI).
Communication with the Checksum team is as easy as dropping a Slack message, even though comms are not often needed as the tests are maintained automatically.
Perhaps more impactful, the Checksum testing suite runs on every PR and engineers can easily run the tests locally. Tests are segmented by priority, so when working on a new feature,
engineers gain immediate feedback on how their changes affect the app across different
configurations and clients.
Checksum saved us $200K a year. The fact that they provide a comprehensive testing suite and maintain it in real-time is a game changer. Our engineering team moves and innovates faster and paying per test allows us to tie the costs directly to the money saved. – Elías Matheus, CTO
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Gal Vered
Gal Vered is a Co-Founder at Checksum where they use AI to generate end-to-end Cypress and Playwright tests, so that dev teams know that their product is thoroughly tested and shipped bug free, without the need to manually write or maintain tests.
In his role, Gal helped many teams build their testing infrastructure, solve typical (and not so typical) testing challenges and deploy AI to move fast and ship high quality software.