A practical evaluation framework for AI code review tools: test finding quality, noise, GitHub ergonomics, policy controls, cost boundaries, and the failure modes a polished demo will not show.
Most AI code review demos are easy to like. A tidy pull request contains an obvious bug, the reviewer catches it, and a concise comment appears on the correct line. That proves the integration can call a model and post to GitHub. It does not prove the tool will help on an ordinary Tuesday, when a repository contains years of conventions, a pull request mixes generated and handwritten files, and the model has several plausible but wrong things to say.
A useful evaluation therefore starts with your repository, not a vendor checklist. Select a small set of previously merged pull requests whose outcomes you understand. Include a real defect, a safe refactor, a dependency change, a test-only change, and at least one large or awkward diff. Remove any review comments if your process allows it, then run each candidate under the same conditions. The goal is not to manufacture a leaderboard. It is to observe how each reviewer behaves when the answer is known.
Counting findings rewards noisy tools. Instead, classify each comment by the decision it creates. An accepted finding leads to a code change or a consciously documented exception. A dismissed finding is incorrect, irrelevant, already handled, or too vague to act on. A useful summary can also help without creating an inline action, but record that separately. This simple ledger makes the central tradeoff visible: ten comments with one useful finding impose more review work than two comments with one useful finding.
The clean pull request is an important test. An AI reviewer should be capable of returning no findings. If every run produces advice, the tool is optimizing for visible activity rather than maintainer attention. Also test documentation-only changes, formatting updates, lockfiles, snapshots, and generated code. A mature system either skips low-value surfaces or lets the repository define how they are treated. Noise from predictable file classes is avoidable noise.
Finding quality is only one part of production fit. Ask what triggers a review, what happens after a force-push, whether an incremental run repeats old comments, and how failed model calls are surfaced. Then trace the billing unit. Per-seat, per-run, token, and provider charges create different incentives. A usable product should make the unit legible, expose run history, and provide a hard or configurable boundary so an unusually large diff cannot create an unexplained bill.
If bring-your-own-key support matters, verify the actual path rather than checking a feature box. Confirm where the provider key is stored, which service sends code to the model, which models can be selected, and whether the application fee is separate from provider usage. BYOK is valuable when it gives a team model choice and spend visibility. It is not automatically cheaper, private, or easier to operate.
Install the best candidate in advisory mode on a few repositories. Do not make it a required check during the pilot. Give maintainers one place to record accepted, dismissed, and unclear findings, and review the sample after enough routine pull requests have passed through. Tune confidence and comment caps before expanding scope. The correct purchase decision is not the tool that found the most in a staged test; it is the one whose useful findings justify the attention, cost, and operational surface it adds.
PR Quorum turns specialist reviewer output into one clean GitHub review, with noise controls and predictable usage caps.