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AI Tool Effectiveness Intelligence

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

Sound familiar?

What teams assume

AI tools are boosting productivity

Everyone's shipping faster. The AI is catching bugs. The investment is paying off. The dashboard says 40% of code is AI-generated.

What's actually happening

Nobody's measured what changed

Code volume went up. Review time went down. But are the reviews meaningful? Are the suggestions landing? You don't have the data to know.

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.

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

Human reviewers average ~90% effectiveness.
Most AI bots land between 45–65%.
Now you can see exactly where the gap is.
Human Reviewers (avg) ~90%
AI Bot A ~63%
AI Bot B ~47%
Effectiveness = comments that led to code changes

The cost adds up.

~12h

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

No workflow changes. Guardian observes. You decide.

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.

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.

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

See your team's actual numbers.

Be among the first teams to measure their Review Tax.

Request Early Access