AI made your developers ship faster. QA became the bottleneck. Proofly clears it — no new hires.
A done-for-you AI QA agent that clears your Ready-for-QA queue — drives your app on staging and posts evidence-backed proof on every ticket. Your team just signs off.
No one kicks it off per ticket — it drains the queue on its own. See it test a real ticket →
The bottleneck moved from writing code to proving it's correct — that's the gap Proofly fills.
Google DORA calls it the “verification tax” — the time saved generating code gets spent auditing it. Proofly is that verification layer — done for you.
“But we already use AI for testing.”
It still costs hours per ticket — a QA engineer drives every step by hand. Proofly runs the same steps automatically, 24/7. Illustrative averages, “Ready for QA” → “Ready for preprod”; real times vary.
| Step · Ready for QA → Ready for preprod | QA engineer + AI tools | Proofly · AI QA agent |
|---|---|---|
| Read & understand the requirements | 15–30 min | automatic |
| Write / adapt the test cases | 30–60 min | automatic |
| Deploy & drive staging | 15–30 min | automatic |
| Execute, reproduce & catch bugs | 45–90 min | automatic |
| Write the proof / evidence | 20–40 min | automatic |
| Update ticket → move status | 5–10 min | automatic |
| Total per ticket | ~2–4.5 hrs of a person | minutes, unattended |
| Your team’s testing time | every ticket, by hand | ~0 — only reviews the sign-off |
| Availability | work hours · queue waits | 24/7 · drains the queue |
Sources: Jellyfish, 37M PRs (2026) · Faros AI (2025–26) · GitClear, 211M lines (2025) · Google DORA (2026). “5×” refers to the review slowdown, not dev speed; figures cited as reported by each study.
Finally — QA keeps up with dev.
One ticket in. One proof out.
The agent tests first — a human on your team verifies the agent. That's the whole model.
Same cause — dev got faster, QA didn't. Here's the pain, and what you get back.
Short answer: No.
Proofly removes the repetitive legwork — deploying branches, clicking the same flows, screenshotting, writing up results. It doesn't replace the people. The agent tests first; a human verifies the agent.
QA doesn't leave the loop — it moves up a level: from typing steps to reviewing evidence, hunting edge cases, and owning the gate. The agent never merges, never ships on its own. A human always signs off.
From a 6-month deployment we ran ourselves (client anonymized under NDA) — real numbers, not yet an independent customer result. Market figures under “Why now” are third-party studies.
Not a green checkmark you have to trust — a full proof: the flow driven, per-criterion evidence, screenshots, and an honest list of what was not covered.
/earn/requests 200, values match UIIllustrative rendering of the real artifact · full proof report → · the board →
Nothing in your process gets replaced — the QA bottleneck just stops being a queue you wait in.
The moment engineering finishes, release no longer sits in a QA queue. That's the change VP Engineering is buying.
The same agent runs at two levels — in the two places bugs actually hide.
As each ticket hits Ready-for-QA, the agent claims it, deploys its branch to staging and verifies it in isolation — proof posted straight to the ticket.
Before you ship, the same agent re-runs every ticket inside the merged build and can run your regression suite — catching a fix lost in a rework, or branches that only conflict once combined. You get a GO / NO-GO report; it never ships on its own.
Gate 2 exists for one class of bug: green on its own branch, broken in the build. In the pilot, a fix lost in a branch rework was exactly this.
Every team is different — capabilities switch on per project.
Drive the real flow on staging and post evidence-backed proof. The always-on core.
Author durable E2E / API tests into your repo and open a Draft MR — following your existing test architecture.
Generate documented test cases and push them to TestRail, TestOps, Qase, Zephyr or Xray.
Re-verify every ticket in the assembled build and run your regression suite before you ship.
A 6-month deployment we ran ourselves, anonymized under NDA — the honest source of these numbers (not yet an independent customer case).
The payoff: the release it passed shipped with zero bugs and zero support tickets — PM signed off that it works.
The agent claimed a reset-password ticket off the queue itself, deployed four coupled branches, drove the real UI through a 2FA gate and an emailed one-time link, checked the endpoint and the database, self-corrected its own test-rig mistake, then filed the proof and moved the ticket on.
One defect it caught — a fix lost in a branch rework — was invisible to a rushed manual pass. That's the release-level bug class no per-branch check finds.
Clearing the Ready-for-QA queue vs adding headcount to do it.
A fraction of one hire — no recruiting, no ramp, no maintenance. You keep the human sign-off; we run the agent.
Hard limits enforced in code, not prose. This is why leaders let it run on their stack.
Fully managed — we run the agent, you get proof. From a low-risk pilot to a QA layer across teams.
The pilot is a fixed 2-week Proof of Value. Managed & Scale are quoted on your queue volume and stack. LLM tokens are billed to you at cost (or your own key); our fee is the managed service on top.
Tell us what to test. We scope a run on your stack, set up scoped access under NDA, and hand back real proof — starting with a short setup step, not a sales pitch.
🔒 Scoped access, under NDA — no "run on prod" button on day one.