Merge faster without merging blind.

AI helps your team write code faster. We find where that code waits before production, and how to get it moving.

Start the conversation Read the real pull request

A 30-minute conversation about your repositories. If we’re the wrong fit, we’ll say so.

31 min from a maintainer’s issue to a tested pull request

14:43 A maintainer opened issue #1795 fix: timeout_seconds does not tear down the dart test process on the Dart runner
15:14
unicoderbot opened pull request #1796 AI agent fix: force-kill dart test subprocess after timeout grace period
- Found the root cause in how dart test handles SIGTERM
- Escalates to SIGKILL after a 2-second grace period
- Adds tests for a clean exit, an ignored SIGTERM, and a zombie process
15:48 unicoderbot pushed a fix for its own failing CI AI agent fix(ci): address failing checks on PR #1796
Now 26 checks passed. Waiting for a maintainer to review. A person still decides what merges.

A real exchange in our open-source Very Good CLI, November 5, 2026. Read the issue and the pull request on GitHub.

Code now reaches review faster than review was built for.

Review queues, CI, and approval rules were set for the old pace, so the time AI saves waits there. Buying more coding seats adds to the queue, and adding reviewers does not tell you which gate to fix first. Every day a finished change waits is a day your customers don’t have it.

What changes about code review when AI writes the code

01Review

The review queue

Pull requests wait until a reviewer has time, and more generated code means more of them in line.
02Tests and CI

The pipeline

Slow or flaky checks turn a small change into a day of reruns before anyone can approve it.
03Approvals

The release rules

Approval rules written for a slower pace hold every change for the most careful sign-off.

You get a measured baseline and a ranked plan.

We read the recent history of up to three repositories and their pipelines, find where changes wait, and rank the fixes worth making first.

Then Unicoder can carry the fixes

01
A baseline you can rerun
You get review time, CI time, and the path to main, measured from the recent pull request history of each repository.
02
A risk map
It shows where review, tests, and release controls meet, and which of those gates catch real problems.
03
A ranked plan
It ranks the fixes worth making first and names what each one depends on.
04
A readout
Your engineering leads walk through what we found and decide what to do next.

Built for

Product engineering teams on GitHub that feel the review queue every week
Up to three repositories to start
Every stack, with our checks tuned most finely for Flutter
Any pull request size, with small, focused ones giving the most accurate read
Read-only access to pull request and CI history, or an export of it

Not built for

Changes to your code or CI, which is custom work
An agent that merges without a person
Delays that are really about product or requirements decisions

Start the conversation

Tell us about your repositories. If the check is the wrong fit, we’ll say so.

Three ways to start.

Read the sample for free, request the check for up to three repositories, or bring us a bigger brief.

01
See it working

The exchange at the top of this page is public. Read the issue and the pull request on GitHub, with no form and no call.

Who it suitsAnyone weighing AI in their review flow

See Very Good CLI on GitHub
02
The PR-to-Prod Check

Your first check covers up to three repositories. Every stack is welcome, and our checks are tuned most finely for Flutter. Any pull request size works, though small, focused ones give the most accurate read, whether a person or a model wrote them.

Who it suitsTeams on GitHub with real review friction

Start the conversation
03
Custom work

Rollout across teams, CI repair, or more than three repositories, scoped with you after a first conversation.

Who it suitsTeams that already know where the wait is

Start the conversation

What teams ask before the check.

Anything else, bring it to the first conversation.

01 Why hasn’t faster AI coding shortened our release cycle?

AI tools write code faster than review queues, CI, and approval rules were built to absorb, so the time saved waits there. The check measures where it waits and ranks what to change first.

02 Do you need access to our code?

For the check, we need read-only access to the pull request and CI history of the repositories in scope, or an export of it. Unicoder runs on your own model API key, and we store nothing about your code. Our security and IP page covers how we handle your code and IP on every engagement.

03 Will this change our code or merge pull requests?

No. The check is a diagnosis. Code changes and CI repair are custom work, and a person on your team always decides what merges.

04 Is it only for Flutter?

No. Every stack on GitHub is welcome. Our checks are tuned most finely for Flutter, because that is where we build most of our work. Any pull request size works, and small, focused pull requests give the most accurate read.

05 How soon can we start?

Teams on the waitlist start first. Unicoder can start the same day as a GitHub App, or within a few days on your own private cloud.

06 What does it cost?

Pricing opens to the waitlist first. It depends on the number of repositories, your pull request volume, and your CI setup. Join the waitlist and we’ll share it first.