EN·ES·PT
Case studies · proof you can open

Four systems. Four measured outcomes. Every one links to the receipt.

No logos I can't back up and no numbers I can't show you. Each of these is a system I built or rescued, with the metric that mattered, and a link to the public repo, the verbatim run, or the live screenshot that proves it.

Fortune-50 test engineering ISTQB Test Automation Engineer Open-source: llm-eval-gate · playwright-sdet This site runs its own QA — 100+ checks, axe-clean
AI quality & evaluation · nexural-qa-os

The QA platform that refused to show a fake green.

red → green CVE gate went red at 11:39, blocked the release, and was back to a proven 13/13 by 14:35. Both runs published verbatim.
The problem

A quality platform is only trustworthy if it can fail. On a real run, the dependency-audit gate found 15 high/critical CVEs. The honest verdict was not shippable, not a green badge.

What I did
  • Let the gate go red and published the verbatim run: no quiet override
  • Fixed the drift at the source: 9 pinned security floors, killing the unpatchable transitive chain
  • Re-ran the full battery: 85 runners incl. 10 LLM-safety evals
The result

3,759 tests · 91% coverage · 13/13 gates. High/critical CVEs: 15 → 0. The whole caught→blocked→patched→proven arc is on the site, both captures included.

▸ the red run (verbatim) ▸ the green rerun verified 2026-08-15 · both runs published
Test automation · playwright-sdet-regression-suite

A regression suite a release manager can trust.

37/37 specs green in CI · 15.3s · 0 flakes — with traces, screenshots, and a written risk model.
The problem

Most suites are script soup: coverage follows whoever wrote tests last, red is ignored because it's flaky, and every failure starts a screenshot scavenger hunt.

What I did
  • Risk model → coverage matrix, so coverage tracks release risk
  • Page Object Model, fixtures, trace-on-retry, four reporters
  • Wired into CI with artifact retention, a green badge that means something
The result

37/37 specs, zero flakes, 15.3s. The evidence folder — traces and screenshots — ships in the public repo. Clone it and reproduce the run.

▸ public repo ▸ the run verified · run dated 2026-07-10, evidence in repo
AI engineering · RAG research dashboard

An AI assistant that can't answer without a citation.

100% of answers cite their source, built into the design rather than requested by prompt. There is no uncited generation path.
The problem

Most RAG demos hallucinate confidently. "Grounded" is claimed in the prompt and quietly violated in production the first time retrieval comes back thin.

What I did
  • Built retrieval so every answer is bound to ranked, selected chunks
  • Removed the uncited generation path entirely: abstain when there's no evidence
  • Tracked quality, safety, latency, and cost per query on an analytics endpoint
The result

100% citation coverage, by construction. Verified live. I ran it, asked a real question, and captured the actual cited answer (screenshot on the homepage).

▸ live screenshot + architecture verified 2026-08-15 · ran live, real query captured
CI rescue · HighStrike (production suite)

Making red mean something again on a live suite.

10% → <1% flake rate on a live production suite — cut with retry logic and test-isolation fixes, no rebuild required.
The problem

A flaky suite is worse than none: when red is noise, the team learns to ignore it, and the real regression sails through with the false alarms.

What I did
  • Diagnosed the flake sources: shared state, timing, ordering
  • Installed a flake protocol: quarantine lane, retry policy, isolation fixes
  • Kept the existing suite and treated rebuild as the last resort
The result

Flake rate 10% → under 1%. Red became a real signal again, carried on the same Fortune-50-grade discipline behind 500+-test infrastructure earlier in my career.

▸ the service this became ▸ full track record from live client engagement · method productized

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