Proofly — Release when engineering is ready, not when QA catches up

Why now

AI writes more code than QA can verify

The bottleneck moved from writing code to proving it's correct — that's the gap Proofly fills.

+79%

more PRs shipped — top AI adopters

Jellyfish, 37M PRs, 2026

+9% → +54%

bugs per developer, and rising

Faros AI, 2025 → 2026

+441%

code-review time — verification is the new queue

Faros AI, 2026

25% → <10%

refactoring, as AI code piles up

GitClear, 211M lines, 2025

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	~0 min of QA timeruns automatically
Write / adapt the test cases	30–60 min	~0 min of QA timeruns automatically
Deploy & drive staging	15–30 min	~0 min of QA timeruns automatically
Execute, reproduce & catch bugs	45–90 min	~0 min of QA timeruns automatically
Write the proof / evidence	20–40 min	~0 min of QA timeruns automatically
Update ticket → move status	5–10 min	~0 min of QA timeruns automatically
Total per ticket	~2–4.5 hrs of QA time	~0 min of QA timeruns unattended
Your team’s testing time	every ticket, by hand	sign-off review onlyminutes, once per ticket
Availability	work hours · queue waits	24/7 · drains the queue

“~0 min of QA time” is time your team spends on the six steps above, not elapsed time — the agent runs unattended. It does not include the sign-off review, our setup with you, clarifying an ambiguous requirement, triaging a false positive, or re-testing after a fix.
Sources, as reported by each study: Jellyfish, AI Engineering Trends — 37M pull requests across 1,000+ companies · Faros AI, The Acceleration Whiplash (2026) — 22,000 developers across 4,000 teams; median time in PR review up 441.5% under high AI adoption · GitClear (2025) — 211M changed lines; duplicated blocks up, refactoring down · Google DORA, ROI of AI-assisted Software Development (2026). The “5×” above is that measured review slowdown — it is not a claim about development speed.

The bottleneck, by role

You feel it differently by seat

Same cause — dev got faster, QA didn't. Here's the pain, and what you get back.

🧭 Head of Eng / CTO

Releases wait on QA. Hiring testers is slow and costly — and bug rate climbs anyway.

Ship faster with the team you have. Throughput scales with the agent, not headcount.

🚀 Product / Delivery

Features are built but stuck in the QA queue. You can't promise dates you don't control.

Predictable delivery. The queue drains around the clock — shipping stops being a coin-flip.

🛡️ QA / Test Lead

Drowning in a manual backlog, can't match dev speed — and you're blamed when quality slips.

Off the treadmill. The agent does the legwork; your people own judgment and the gate.

The honest answer

"Does this replace my QA team?"

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.

Show, don't tell

This is what lands on every ticket

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.

MA-3112 · comment · Proofly QA Agent

🐝

Proofly QA Agent · moved to “AI Agent: Done”

VERDICT: PASS

Withdrawal request appears in the Earn block — ✓ driven on staging, screenshot #2
Amount & status render correctly — ✓ API /earn/requests 200, values match UI
Confirmation email link resolves — ✓ followed one-time link, endpoint OK

📎 full-page.png📎 earn-block.png📎 db-check.txt

Env: dev-web.stage · deploy #4471 · Visual/layout: ✓ vs baseline · Not covered: real bank 3DS leg (needs human)

QA board · after one run

Ready for QA · 0

— queue drained —

AI Agent: Done · 3

MA-3112

Earn withdrawal request

PASS

MA-3108

Reset password via 2FA

PASS

MA-3115

Balance widget rounding

BUG CAUGHT

Illustrative rendering of the real artifact · full proof report → · the board →

What changes

Your release flow — without the QA wait

Nothing in your process gets replaced — the QA bottleneck just stops being a queue you wait in.

Today

Release waits on QA capacity

👩‍💻 Developer finishes

📋 QA queue days

🖱️ Manual testing slow

🚢 Release

→

With Proofly

Release stops waiting on the QA queue

👩‍💻 Developer finishes

🐝 Proofly verifies + proof hours

👀 Human review & sign-off fast

🚢 Release

The moment engineering finishes, release no longer sits in a QA queue. That's the change VP Engineering is buying.

One agent, two gates

Test each change. Then test the whole release.

The same agent runs at two levels — in the two places bugs actually hide.

gate 1 · per ticket

On the feature branch

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.

gate 2 · per release

In the assembled build

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.

Turn on what you need

Verification is the core. The rest is optional.

Every team is different — capabilities switch on per project.

✅

Verify & prove

Drive the real flow on staging and post evidence-backed proof. The always-on core.

🧪

Write autotests

Author durable E2E / API tests into your repo and open a Draft MR — following your existing test architecture.

📋

Test cases → your TMS

Generate documented test cases and push them to TestRail, TestOps, Qase, Zephyr or Xray.

🔁

Regression + release gate

Re-verify every ticket in the assembled build and run your regression suite before you ship.

From our own deployment

A real deployment, real numbers

A 6-month deployment we ran ourselves, anonymized under NDA — the honest source of these numbers (not yet an independent customer case).

Fintech trading backoffice

38 developers

3 QA

Jira · GitLab · TeamCity

6 months

Before → after

Same team, same stack, agent added.

Before: ~3 tickets verified per working day, by hand.
After: 17 tickets in a single agent session — a week's worth in a day.
Test authoring went from ~3/month to ~56/month — ~18× the monthly volume. That is throughput; we did not measure time per individual test.
Bug inflow stayed flat (~20/mo) while delivery sped up.

The payoff: the release it passed shipped with zero bugs and zero support tickets — PM signed off that it works.

The worked example

One ticket, end to end, unattended.

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.

The math

The math vs hiring

Clearing the Ready-for-QA queue vs adding headcount to do it.

What you'd need without ProoflyAnnual cost

1 QA engineer, fully loaded (salary + benefits + overhead)$130–150K

Hiring + 3–6 months to fully productiveOpportunity cost

Releases sitting in the QA queue, waitingOpportunity cost

Total$150K+/yr

Proofly — 2-week pilot, one-off, to prove it on your stackQuoted

Proofly — ongoing managed serviceQuoted monthly

The pilot is a paid two-week trial, not an annual figure — the two rows above are not a like-for-like saving. Both the pilot and the ongoing service are quoted on your queue volume and stack, with LLM tokens billed at cost. What Proofly removes is recruiting and ramp-up, not every QA cost: you still keep the human sign-off.

Safe to run unattended

The rails the agent can't cross

Hard limits enforced in code, not prose. This is why leaders let it run on their stack.

✕Never merges, closes, or ships. It proposes and proves — a human approves.
✕No production, ever. Only disposable staging environments.
✕No direct writes to your database. The agent connects read-only. Anything it changes, it changes through the app under test — on disposable staging, and reverted within the run.
✕Runtime proof only. A PASS cites the endpoint and response — no "looks fine".
✕Scoped access under NDA. You grant only what it needs; revoke any time.
✕Budget caps + kill switch. It can't run away or burn tokens unbounded.

Ways to start

Start small. Scale when it's proven.

Fully managed — we run the agent, you get proof. From a low-risk pilot to a QA layer across teams.

Pilot

Prove it on your stack

Custom / two weeks

3–5 critical flows, one project
Wired to your board & staging
Proof reports + a real bug list
Two weeks, then you decide

Start a pilot

Most teams start here

Managed

The QA queue, handled

Custom / monthly

Agent owns the Ready-for-QA queue
Per-ticket and per-release gates
Verify + durable regression coverage
Tuning & support by a QA Lead
No lock-in

Get a quote

Scale

Multiple teams / your keys

Custom

Multiple boards & teams
Bring your own LLM key or self-host
Priority tuning & SLAs
Onboarding playbook for your org

Talk to us

The pilot is a fixed two-week Proof of Value; all three are quoted on your scope, queue volume and stack. LLM tokens are billed to you at cost (or your own key); our fee is the managed service on top.

Who runs it

You're granting access to people, not a logo

Before you give anyone scoped access to your stack, you should know who is on the other side.

Filipp Petrovski

Co-founder — builds and runs the agent, and sets up every client. AI architect and QA lead, 12+ years in full-stack testing, automation and release ownership across crypto trading, fintech and enterprise platforms.

LinkedIn ↗

Maksim Petrov

Co-founder — product and business development, and the one who runs the demo call. 15+ years building products and small businesses across e-commerce, marketplaces and lead generation, from requirements through launch.