Code review that catches the boring bugs
before a human has to
Pull requests pile up while reviewers are busy with their own work. The comments that do land are often the same ten issues repeated for the hundredth time. We build an agent that reads every diff first, flags the boring and the risky, and leaves the judgment calls to your team.
Why a review queue grows faster than reviewers can clear it
A pull request sits open for hours before anyone looks at it, and the delay compounds because the next PR builds on the first one. Engineering surveys across mid-size teams commonly put median time-to-first-review anywhere from a few hours to more than a day. That gap is where context gets lost. The author moves on, the reviewer forgets the details, and a second round of comments takes just as long to land as the first. Most of what a senior engineer spends that time on isn’t architecture. It’s the same handful of issues on repeat: an unhandled error path, a missing test, a style rule the linter should have caught. Or a hardcoded value that should be a config setting.
Security issues are the ones that hurt most when they slip through a rushed review. A secret committed by accident, a query built from unescaped input, a dependency bump that quietly drops a security patch. None of these need a human’s judgment to spot. They need someone to actually read the diff line by line every single time, which is exactly the part reviewers skip when the queue is long. Teams either accept the risk, or they slow releases down with mandatory multi-reviewer rules that create the same backlog from a different angle.
What the agent catches before a human opens the diff
Reads every diff as soon as it opens. The agent pulls the pull request, the linked ticket or spec if there is one, and the relevant parts of the codebase around the change. Then it comments directly on the lines that matter, not a generic top-level note.
Checks against your actual style guide and linters, not a generic best-practices list. Your ESLint or Ruff config, your naming conventions, your team’s own patterns learned from merged pull requests.
Flags security patterns a linter misses. A secret in a config file. An unescaped query. A missing input check on a new endpoint. A dependency bump that touches a package with a known CVE.
Scores risk per diff. Reviewers know which five pull requests in a queue of twenty need their full attention. They also know which three are safe to approve on a quick read.
Flags test-coverage gaps before merge. It points at the new branch or edge case that shipped without a test, not a blanket coverage percentage.
Summarizes the change in plain language for a reviewer who wasn’t in the original conversation. It also posts a weekly digest of the issues that keep recurring across the team, so a lead can fix the root cause once.
What stays with your engineers
The agent comments. It does not approve or merge. Every architectural tradeoff, every subjective style debate, and every decision to accept a risk the agent flagged stays with your engineers. Anything the diff touches around authentication, payments, data migrations or infrastructure config gets routed straight to a human reviewer first. If a reviewer disagrees with a flagged issue, dismissing it takes one click and feeds back into the tuning.
Guards
Before go-live, a dry-run period has the agent comment into a private channel instead of the actual pull request. Your team can check its judgment against real diffs before your own developers ever see an automated comment. After that, every comment logs the rule or the reasoning behind it, so nothing is a black box. Rate limits keep the agent from flooding a large pull request with noise. A kill switch pulls it off any repo instantly. The routing rule for sensitive code (auth, payments, migrations, infra) is fixed before launch. It doesn’t change without you approving that yourself.
Price and timeline
| Package | Price | Best for |
|---|---|---|
| Single automation | from $800 | One repo, one agent wired into your existing pull-request workflow and linters |
| Department package | from $2,500 | Code review plus several more development automations: test generation, release notes, log triage, documentation |
4 to 10 days for the first repo. Most of it goes to matching your actual style guide and running the dry-run period, so the first live comments are already tuned to your codebase.
Related
Pairs naturally with test generation, release notes and log triage and alerting for the rest of the development pipeline. Part of automation of everything digital and built the way we build AI agents for our own products.
Two of our own projects ran under this level of review discipline. The AI sales agent with 846 unit and 48 integration tests is one. TaskWall is the other, reviewed by three specialised agents covering Kotlin, Swift and JS.
Tell us which repos and languages are in scope and we will send back a fixed price and a plan for the first week: get in touch.
Tired of doing this by hand? We can take the whole routine off your team, not only this step: Routine takeover, from $400 →
FAQ
How much does an AI code review agent cost?
A single-repo agent wired into your existing workflow starts from $800. A department package covering code review plus several other development automations starts from $2,500. The exact number depends on how many repos and languages are in scope.
How long does it take to go live?
4 to 10 days for the first repo. We connect to your git provider and match your lint rules and style guide. Then comes a short dry-run period where the agent comments but a human still checks every note before it ships.
Which tools does it connect to?
GitHub, GitLab and Bitbucket for pull requests. Your existing linters and static analysis tools, ESLint, Ruff, SonarQube and similar, plus your CI logs for test results. Claude handles the logic and design issues a rule-based linter can't catch.
What if the agent gets a review comment wrong?
It posts a comment, not a merge decision. Every comment names the rule or the reasoning behind it, so a reviewer can dismiss it in one click. We tune the rule set against your real pull-request history before launch to cut down on noise.
Is our source code safe?
The agent reads only the repositories you connect it to, and runs under your own git provider's permissions. We do not retain your code outside the review session. Keys and tokens are yours and can be revoked at any time.