Code Quality
AI Code Review Agent for GitLab
Reviews pull requests for bugs, security issues and standards and comments inline, connected natively to GitLab so it reads and updates projects,…
Code Quality
AI Code Review Agent
Every pull request gets reviewed for bugs, security issues and your team's standards, with inline comments before a human looks.
Code review slows down when reviewers are busy, and quality depends on who happens to be free. AI Code Review Agent reviews every pull request for bugs, security issues and your own coding standards, then posts inline comments on GitHub, GitLab or Bitbucket. Your engineers accept, edit or dismiss each comment. Nothing merges and nothing reaches production without a human's approval.
- Every pull request checked against your own coding standards
- Inline comments where engineers already work, not another dashboard
- Comments only, with no merges or production changes
$3,900
20–30 working days
Optional maintenance $249/mo
What it is
AI Code Review Agent reviews every pull request in your repository for bugs, security issues and your own coding standards, then posts inline comments on GitHub, GitLab or Bitbucket for a human to accept, edit or dismiss. It never merges or changes production code.
Why businesses buy it
Slow or uneven code review is expensive. Pull requests wait for a free senior engineer, small security issues slip through on busy days, and standards drift between teams. AI Code Review Agent gives every pull request a consistent first pass in minutes, so human reviewers focus on design and logic instead of repeat findings, and nothing gets missed because someone was rushed. The Scope Card fixes the repository, connected systems, review workflow and trial on real pull requests before you order, so engineering knows exactly what will run.
Problems this solves
Pull requests wait too long for someone to give a first review
Every pull request in the connected repository gets an automatic first pass. Inline comments arrive soon after it opens, so human reviewers start with the obvious issues already flagged.
Rushed reviewers miss bugs and security problems
The agent checks each change for likely bugs and common security issues, not just style. Findings are posted as inline comments on GitHub, GitLab or Bitbucket for a person to accept, edit or dismiss.
Coding standards live in a document few people re-read
Your own coding standards are built into the agent's configuration. It comments when a change breaks them, so standards get applied during review rather than remembered afterwards.
Teams worry about an AI changing code without approval
The agent only comments. It never merges or changes production code, and every suggestion waits for a human decision. A trial on 20 pull requests lets you judge its comments first.
A good fit if
Your team opens pull requests regularly, and reviews either take too long or catch different things depending on who is free.
You have written coding standards or runbooks, even informal ones, that a review should be checked against every time.
You want findings posted as comments on the pull request itself, in GitHub, GitLab or Bitbucket, not a separate dashboard nobody opens.
You are comfortable with the agent commenting only, with no production changes made without a human's approval.
Not the right fit if
You want automated test runs and defect reports, not code review comments. See AI QA Agent, built for that.
You want new tests written for existing code, not review of new pull requests. See AI Test Generation.
You mainly want routine dependency upgrades proposed as pull requests, not general code review. See AI Dependency Update Agent.
You need broader CI/CD and infrastructure monitoring, not pull-request review specifically. See AI DevOps Agent.
You want designs and architecture changes threat-modeled for security before code is written. See AI Security Review Assistant.
How it works
- You give us scoped, read-and-comment access to one repository, your CI/CD configuration and your coding standards or runbooks.
- The agent watches for new and updated pull requests in that repository and reads the changed code against your own standards, not a generic style guide.
- It flags bugs, security issues and standards violations directly, with an inline comment on the exact line, explaining what it found and why.
- Findings can also be posted to your issue tracker or chat, such as Jira, Linear or Slack, if you want a running record outside the pull request.
- A human always decides what happens next. The agent comments and suggests; it never merges, approves or changes production code itself.
- We trial it on 20 real pull requests or pipeline runs before launch, so you see its judgment on your actual codebase first.
What you provide within 72 hours
Your portal lists these the moment you order. Delivery starts the working day after everything is in and your access is checked.
- Scoped repository access, as an app or token, limited to read and comment, not merge or admin rights.
- Your CI/CD configuration details, so the agent understands how builds and checks already run.
- Your coding standards or runbooks, even informal ones, for the agent to check pull requests against.
- Access to your issue tracker or chat tool, such as Jira, Linear or Slack, if findings should post there too.
- Admin access and API keys for every connected system, shared by adding our team as a user or creating a key you control, never by email.
- Your business information (company basics, hours and brand voice), shared during intake.
- One decision-maker who can approve the review rules before launch.
- An API key for your own AI model account (for example OpenAI or Google Gemini), created for this build with a spending limit.
The 72-hour rule. Send everything on your checklist, including access, within 72 hours of ordering so your build starts on time.
Timeline
Start
Intake confirmed, with repository access, coding standards and CI/CD details agreed.
Weeks 1–2
Review rules built from your coding standards and connected to your repository.
Week 3
Comment posting and, if chosen, tracker or chat integration connected and tested.
Weeks 3–4
Trialled on 20 real pull requests, with findings reviewed together.
Week 4
Your review and two rounds of changes.
Go-live
Live, with a recorded handoff session, and 30 days of support starts.
| Option | Delivery time | Price |
|---|---|---|
| Standard | 20–30 working days | $3,900 |
| Express | 15–25 working days | $4,600 |
| Rush | 10–20 working days | $5,250 |
What you receive at handover
- The code review agent connected to one repository on GitHub, GitLab or Bitbucket
- Your coding standards built into its prompt and guardrail configuration
- Connection to one more system, such as your tracker or team chat
- Test report from a trial on 20 pull requests
- Admin guide and a recorded 45-minute handoff session
- 30 days of post-launch support
Expected benefits and how to measure them
- Can shorten the wait between opening a pull request and getting useful feedback.
- Helps senior developers focus their reviews on design rather than routine issues.
- Makes it easier to apply the same standards to every change.
- Time to first review comment
- Use your Git host's pull request history to compare the gap between opening and first comment, a month before and after launch.
- Review rounds per pull request
- Count review cycles on merged pull requests for a month before launch and a month after.
- Bugs found after merge
- Count bugs in your tracker traced back to merged changes, for the month before launch and each month after.
Before you start: Pull a month of pull request history and note the time to first review and the number of review rounds. Record how many post-merge bugs were logged.
These are things to track, not promised results. Outcomes depend on your data, your team and your market.
Running costs
You pay for the build once. Running the agent costs roughly $20 to $200 a month in your own accounts, covering AI usage and CI minutes, and it moves with how many pull requests you open each month.
AI usage during the build and testing is billed to your own accounts too, so your AI model and CI relationships stay in your name.
Ask about running costsCustom requirements
If you need a second repository, more than 2 connected systems, or a second review workflow, add a note at checkout for a written Change Quote. Your order still runs on its normal timeline. Larger or bespoke projects go through the Custom AI request form.
Request a custom versionKeep it running
Monthly maintenance covers monitoring, fixes, model and platform updates, a monthly health report and monthly hours for changes, covering updated standards or a new integration. It starts after the 30 days of support and can be cancelled any month.
$249/mo (the minimum fee, includes 2 change hours)
Optional. Starts after the post-launch support period. Cancel any month.
How maintenance worksHow this compares
| Do it yourself with a SaaS tool | Hire a custom agency | Upmark Labs | |
|---|---|---|---|
| Price | A code-review SaaS tool charges per seat every month, reviewing or not | A contract reviewer or agency bills by the hour | Ours is one fixed price, shown before you buy |
| Standards | A code-review tool ships with generic rules you configure yourself | An agency reviewer learns your standards as they go | Ours is built around your own coding standards from day one |
| Where it comments | A code-review tool often needs a separate dashboard to check | An agency reviewer comments wherever they were asked to | Ours posts inline comments directly on the pull request |
| Delivery | A code-review tool is instant to sign up but slow to tune well | An agency engagement usually takes several weeks to ramp up | Ours has a fixed delivery time shown before you buy, with faster options |
Questions about this product
How is AI Code Review Agent different from AI QA Agent?
AI Code Review Agent reviews pull requests for bugs, security issues and standards before code merges. AI QA Agent runs structured test plans against your running application and reports defects with evidence. Many teams use both together; choose Code Review first if pull requests are your main bottleneck.
Can AI Code Review Agent merge or change code on its own?
No. It posts inline comments and suggestions on the pull request, and a human decides what happens next. No production changes are made without approval, which is a fixed part of the build, not a setting you have to remember to turn on.
Will it learn our specific coding standards, or just general best practice?
It reviews against your own coding standards or runbooks, supplied during intake, not a generic style guide. If your standards are informal or undocumented, describing them during intake is enough; we do not require a polished style guide before starting.
How much does AI Code Review Agent cost to run each month?
Roughly $20 to $200 a month in your own accounts, covering AI usage and CI minutes, and it moves with how many pull requests you open. The build itself is paid once and covers connecting your repository and building the review rules.
Which Git hosts and trackers does AI Code Review Agent work with?
GitHub, GitLab and Bitbucket for the repository, plus Jira, Linear or Slack as an optional second connected system for findings. You choose your Git host and, if you want one, a tracker or chat tool during intake, up to 2 connected systems in total.
How long before AI Code Review Agent is reviewing our pull requests?
Delivery runs on a fixed schedule from a complete intake, and the delivery panel shows that date plus express and rush options before you buy. Most of the time goes into building review rules from your standards, connecting your repository, and trialing it on 20 real pull requests before launch.
Every pull request reviewed against your own standards.
Order AI Code Review Agent and it goes live on a fixed delivery date, commenting on pull requests against your own standards, never merging them.
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