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A major enterprise SaaS platform was caught in a cycle of constant firefighting. 60% of their QA team’s time went into keeping automated tests alive, leaving little room to expand coverage or reduce their heavy manual testing workload. With Octomind’s AI-powered auto-maintenance, they reduced maintenance time by 83%, stabilized a growing test suite, and created the space to restart automation at scale.
“I didn’t believe it at first, but auto-fix is an absolute game changer.”
Rajesh, Senior SDET
This company’s platform evolves quickly, with thousands of enterprise users relying on weekly releases to run their operations. But behind the scenes, the QA team was under constant strain. Out of 5,500 functional end-to-end tests, half were automated - and even with that level of automation, keeping the suite healthy had become a challenge in itself.
Maintaining the 2,500 automated tests consumed so much time that there was no bandwidth left to add new ones. Manual testing stayed high simply because automation progress had stalled. Pipeline failures were a constant interruption, triggered by flakiness, small application changes, or outdated selectors. Fixing them wasn’t straightforward either, inconsistent coding styles across engineers meant that even small updates could require untangling complex dependencies before a repair could be made.
Video recordings were uploaded to TestRail to support failure analysis, but each failing test still had to be reviewed manually, often by someone unfamiliar with it. That meant hours spent investigating, context switching, and delaying releases - all while the next set of failures was already piling up.
“We were stuck in reactive mode,” recalls Hendrik, Senior Manager, Developer Productivity. “The pipeline dictated our schedule, not the other way around.”
The CTO wanted to eliminate the root cause: the time and effort spent diagnosing and repairing failing tests. Octomind’s AI-powered auto-maintenance provided exactly that.
From the first run in the pipeline, test failures came with a plain-language summary describing the cause, so any engineer could quickly understand the problem without sifting through logs. Each failure was automatically categorized, whether it was a broken dependency, a slow page load, an application bug, or a selector change - giving instant clarity on what needed attention.
The “last successful run” feature made it easy to compare passing and failing versions of a test, pinpointing the exact change that caused the issue. From there, Octomind’s AI proposed a targeted fix, ready for immediate approval. This transformed failure handling from hours of debugging into a quick, predictable routine, keeping the pipeline flowing and the suite stable.
“Thanks to Octomind, we can analyze failures so much faster. And the consistency in how tests are generated means you see the problem right away.”
Hendrik, Senior Manager, Developer Productivity
The real breakthrough: the AI then suggested a targeted fix, ready for quick approval. This turned what used to be hours of manual debugging into minutes, keeping the suite stable and freeing the QA team to expand automation coverage instead of constantly patching existing tests.
Future of testing is QA - automated, AI-powered, and built for speed. Plug Octomind into your pipeline and catch bugs before your users do.
