Case study · From the Pluris libraryReceipt no. 2257
What sucked
Developers had AI coding tools and external support, but adoption was inconsistent with no governance model for AI-generated artifacts.
A legacy monolithic architecture created slow compile times, regression risk, and handoffs, while QA ran downstream rather than in parallel.
What they built
Eliza designed and implemented a governed AI-enabled software delivery lifecycle covering planning, implementation, pull requests, review, remediation, and validation, adding prompt/workflow patterns per team plus governance standards for AI-generated plans, code, tests, and PRs, with human approval points and audit logging.
What changed
4 days ≈ 1 month
AI Foundry exercise delivered ~a month of sprint output
600 QA tests
Playwright tests generated via AI-assisted QA, running in parallel
Pod 30 → 9
Proposed model reduces staffing to 9 engineers/pod (proposed, not realized)
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