01
Operating-model beachhead
The first 30 days are about establishing the AI-native pattern: tests as intent specs, AI accelerating implementation, engineers owning verification. Loop's engineer ships the first reference flows your team will copy.
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PPL-001People
Embedded QA
A senior SDET on your team, beachheading AI-native quality.
Engagement
People
Delivery
Continuous engagement
Code
PPL-001
What you buy
An on-team engineer who lives the operating model.
The promise
Within 90 days you'll have a working example of AI-native quality engineering inside your team. And a teammate the rest of QA can learn from.
Who it's for
QA leaders who've tried staffing-firm SDETs, watched them fail to lift the bar, and want a mentor-grade engineer instead.
Buyer state. “We need someone who can show our team what AI-augmented testing actually looks like.”
What's inside
What we focus on.
02
Automation leverage
We don't add to your test suite. We change its shape. Replacing brittle UI tests with API/integration tests, fixing testability instead of mocking around it, generating Playwright tests from acceptance criteria when it actually compounds.
03
Quality intelligence
We instrument the suite so future decisions are data-driven: flaky failure clustering, escape-rate dashboards, regression drag tracking. Your QA function starts producing the truth about quality, not just running checks.
04
Hiring profile shift
By the end of the engagement, your hiring rubric for QA looks different: AI fluency, system thinking, code testability instinct. Loop's engineer leaves an interview loop and a calibration deck so the bar holds after they leave.
Deliverables
You'll walk away with all of this.
- Senior on-team SDET (full-time-equivalent)
- Reference automation patterns shipped into your repo
- Quality intelligence instrumentation
- Updated QA hiring rubric + interview loop
- Weekly leverage report
The boss-approval frame
How to get this approved.
“We need to upgrade what QA looks like inside this team without firing anyone or buying yet another tool. Loop drops in a senior, mentor-grade engineer who shows the team what AI-augmented quality actually looks like. And we keep them only as long as they create leverage.”
ROI logic. Embedded QA pays for itself if it (a) prevents one bad senior hire, (b) eliminates one major regression-drag pattern, or (c) successfully transitions one teammate into the new function.
Up the ladder · Sideways · Related
Where buyers go from here.
People · PPL-002
Managed Hiring
Tech screenings for companies who don't yet know how to hire for AI.
See it →Audit · AUD-001QA Leverage Review
A one-day private review of your QA team, process, automation, dashboards, and constraints. Through a leverage lens.
See it →Sprint · SPR-001Quality Transformation Sprint
A 6–10 week implementation sprint where we work alongside your team to move QA from test execution to a higher-leverage quality operating model.
See it →