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Your AI tools tripled your PRs. Did they triple your shipping speed?

The answer is in your data. You just haven't measured it yet.

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Sound familiar?

The CFO asks what your AI tool spend delivered this quarter. You have developer sentiment surveys. Not data.

— VP of Engineering

PRs tripled. Review queues backed up. Your best engineers are stuck reviewing instead of building.

— CTO

Your AI bot reviewer generates 20 comments per PR. You resolve them in bulk. How many actually mattered?

— Tech Lead
We named it

The Review Tax

AI tools accelerated code generation. But review didn't scale. The result: your fastest developers wait for reviews. Your best engineers spend hours processing bot noise. Individual speed went up. Organizational velocity didn't.

That gap has a name now.

"Individual speed is not organizational velocity."

We measured it.

Reviewer effectiveness: the percentage of review comments that lead to actual code changes.

~0%
Human Reviewers
<0%
AI Bot Reviewers

That gap is where your engineers lose hours every week.

The cost adds up.

8–12 hrs/week

Senior engineer time spent reviewing AI-generated noise instead of building.

$45K+

Annual AI tool spend with no evidence of effectiveness.

40–70%

AI bot review comments that lead to zero code changes.

Meet Guardian

The metric your current tools don't measure.

Guardian analyzes your pull request history and shows you which reviewers — human and AI — leave comments that actually lead to code changes. No code access. No guesswork. Just your Git history, measured honestly.

Request Early Access
Guardian Dashboard — reviewer effectiveness overview
How It Works

How it works

01

Connect

Connect your Git repository. No code changes required.

02

Analyze

Guardian analyzes months of your PR and review history. Real data from real repositories.

03

Know

Receive reviewer effectiveness scores, team profiles, and AI tool benchmarks. Data, not opinions.

Read-only access. No code changes required. First analysis in 48 hours.

Under the Hood

How Guardian measures

Guardian reads your PR history and builds a causal model of your code review process. No agents in your CI pipeline. No code changes required.

What counts as an "effective" review comment?
A review comment is effective when code changes follow it within the same PR. Guardian parses diff hunks to identify what changed between the comment and the next commit, attributing changes to specific comments while accounting for rebases and squash merges.
How do you handle valuable rejections?
Not all "no change" comments are noise. Guardian classifies review patterns across hundreds of PRs to distinguish between comments that correctly approve code and comments that are ignored. Context matters — and Guardian reads it.
What data does Guardian access?
PR metadata, review comments, and commit diffs. Guardian never reads your source code content. Read-only permissions. Your data is retained for 30 days and never shared.

Measured in production.

A 40-person engineering team connected Guardian and discovered their AI review bot had a 34% effectiveness rate — less than half the human baseline. Within 30 days, they reconfigured their bot rules and recovered an estimated 12 hours per week of developer attention.
— Engineering team, 100+ developers
Read-only access
No code stored
Data retained 30 days

See your team's actual numbers.

Be among the first teams to measure their Review Tax.

Request Early Access Schedule a walkthrough