The perception gap
19% slower
Experienced developers took 19% longer on real tasks with AI assistance, while estimating they had been about 20% faster.
METR, 2025 randomized controlled trial (16 developers, 246 tasks)
safe AI adoption
Adopt AI without losing control.
A safe on-ramp from 0% to 100% AI, with your best humans as the baseline and one ruler for the whole team. Trust built with data at every step, so you scale what works and pause what doesn't.
Picture the transition as a dial, not a switch. Releezy Guardian measures every contributor on the same ruler, Releezy Loop runs agents under governance you control, and Releezy Reviewer is held to your human baseline. You turn the dial exactly as fast as the evidence supports.
the problem
The pressure to adopt is real. The instruments are missing.
Your board wants AI in the workflow. Your engineers are already experimenting. And the honest answer to "is it safe to go faster?" is usually a shrug, because perception is a poor instrument. In a randomized controlled trial by METR, 16 experienced developers took 19% longer to finish real tasks with AI assistance, while estimating they had been about 20% faster. Feeling fast is not the same as being fast.
The trust gap is just as wide. In Sonar's 2026 State of Code Developer Survey of 1,149 developers, 96% said they do not fully trust the code their AI tools produce — yet only 48% verify it before committing. Adoption is moving faster than verification, and that distance is where incidents live.
None of this means stop. It means measure. An independent LinearB analysis of 8.1 million pull requests across 4,800 engineering teams found AI-generated PRs are accepted 32.7% of the time, against 84.4% for human-written ones. Teams that see that gap on their own scoreboard can close it, tool by tool. Teams that don't are scaling on hope.
The verification gap
96% / 48%
96% of developers do not fully trust the code their AI tools produce. Only 48% verify before committing.
Sonar, 2026 State of Code Developer Survey (1,149 developers)
the on-ramp
From 0% to 100%, one measured step at a time.
Not a leap of faith. A dial you turn with evidence in hand.
01
Set the baseline.
Releezy Guardian reads your git repository history — read-only, your code never leaves your repository — and computes how effective your best human reviewers already are. That number is your ruler, fixed before any agent writes a line.
02
Start small, fully governed.
Releezy Loop runs your first agents inside isolated containers, under spending limits, with an audit trail and a human review queue on every run. Nothing merges without your team's eyes on it.
03
Compare against humans, not promises.
Every agent run and every AI reviewer — including Releezy Reviewer — lands on the same scoreboard as your humans. Above the baseline earns more work. Below it gets paused and recalibrated.
04
Scale what the data trusts.
Turn the dial. Expand agents to more repositories and heavier work as the evidence accumulates. Speed follows trust.
how releezy helps
Three modules, one safe path.
Measurement first, governance always, and a standard that never bends.
Releezy Guardian
The ruler that never moves.
Releezy Guardian measures every contributor — human or agent — on the same scoreboard, computed from your own git history. Your best humans set the standard, and the standard never bends to flatter any tool, including Releezy's own modules.
Explore Releezy GuardianReleezy Loop
Agent runs you can govern.
Releezy Loop puts agents to work inside isolated containers, under hard spending limits, with an audit trail and a human review queue on every run. You decide what runs, where, and how much it can spend — and you can pause any of it, any time.
Explore Releezy LoopReleezy Reviewer
Held to the human standard.
Releezy Reviewer reviews pull requests knowing your project's rules — and it shows up on the scoreboard next to your humans from its very first comment. If it falls below your baseline, you see it immediately.
Explore Releezy Reviewerwhat changes
What a safe on-ramp feels like.
A dial, not a debate.
Adoption decisions stop being opinion battles. The scoreboard shows which tools earn more work and which need a pause — and everyone can see why.
Speed without the fog.
You expand AI across the team as fast as the evidence supports, with the audit trail and controls to answer any question about any run.
A team that trusts the transition.
Your engineers see that humans set the standard and every agent is held to it. First skepticism. Then evidence. Then adoption.
Questions teams ask before the on-ramp.
Do we need to pause the AI tools we already use to start?
No. Releezy Guardian measures the tools you already use, straight from your git repository history. Start by seeing where you stand today — then decide, with data, what to scale and what to rethink.
What happens if an agent underperforms our human baseline?
You see it on the scoreboard, and you can scale it back or recalibrate it. That includes Releezy Reviewer: the standard never bends for any tool, and a score below your baseline is visible to you the moment it happens.
How fast should we go from 0% to 100%?
There is no universal pace, and that is the point. The on-ramp exists so the pace is yours — set by your own evidence, not by a vendor's promise. Some teams turn the dial in weeks, others take quarters. Both are safe when the ruler is watching.
Is our code safe during all of this?
Releezy Guardian reads your git repository read-only; your code never leaves your repository. Releezy Loop runs agents in isolated containers with spending limits and an audit trail on every action, so every run is contained and accountable.
Start the on-ramp.
A 30-minute demo on your repository. You will see your baseline — and your first safe step — on day one.