Fidelity:
50,000 tests, every sprint.
A brokerage platform was sustaining full test coverage with a single QA engineer against seven developers. Incipient placed an embedded SDET who rebuilt the entire QA architecture in open source. Regression dropped from hours to minutes, and the team now authors tests four times faster with AI.
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One QA engineer, seven developers, 50,000 tests.
A brokerage platform running equities, options, margin trading and digital assets was holding full in-sprint test coverage with a single QA engineer against a seven-developer pipeline. In that setting a missed regression is a compliance risk, not just a quality one.
Commercial tooling carried recurring cost with no path to scale, and Kafka event-stream dependencies blocked downstream testing.
- 1One QA, seven developersFull in-sprint coverage rested on a single QA engineer.
- 2Regression is a compliance riskA missed regression on a brokerage platform is more than a quality issue.
- 3Hours-long regressionThe 50,000-case suite took hours to run.
- 4Licensed tooling, no scale pathCommercial tooling carried recurring cost and could not scale.
- 5Kafka dependenciesEvent-stream dependencies blocked downstream testing.
An open-source QA architecture, built in-sprint.
Incipient placed a senior SDET embedded full-time inside the sprint teams, so the capability sat in the workflow rather than next to it, and rebuilt the QA stack from first principles.
- Built a custom open-source BDD framework (Java, Cucumber, JUnit) running natively in Kubernetes pods through Jenkins CI/CD, with zero licensing
- Used Kafka service virtualization to decouple test execution from downstream microservices, for true shift-left API validation
- Added a parallel pod execution engine that scaled the 50,000-case suite to minute-level regression runs
- Wired Copilot AI agents into Jira stories and the framework repo for story-to-script generation and automated failure triage across MariaDB, MongoDB and CockroachDB
100% coverage, sustained, with no added headcount.
Test authoring accelerated 4× through AI-assisted generation, absorbing the 7:1 developer-to-QA story volume inside active sprints.
The full 50,000-case regression suite dropped from hours to minutes on parallel Kubernetes pods, commercial licensing was eliminated entirely, and Kafka dependencies were unblocked at shift-left, with automated failure triage running across three databases.