Releezy Guardian
Understand what your AI actually changed.
Guardian turns your GitHub PR history into evidence: who reviews well, which tools deliver, and where your team should invest next.
What Guardian measures
Four metrics that give you full visibility into your code review process.
Reviewer Effectiveness
The percentage of review comments that lead to code changes. Measured for every reviewer on your team, human and bot alike.
Team Profiles
Each contributor profiled by strength: security, testing, architecture, business rules. Know who to route reviews to. Growth-oriented, never punitive.
AI Bot Benchmarking
Side-by-side comparison of bot reviewer effectiveness against your human baseline. CodeRabbit, Copilot, custom bots. See the real numbers.
Trend Analysis
Track how effectiveness changes over weeks and months. Correlate with team events: new tool adoption, team changes, process updates.
Technical Depth
A data pipeline. Not an AI wrapper.
Unplug the LLM. Guardian still knows your team.
Most analytics tools feed your data to a language model and return the summary. Guardian is different. The core analysis is statistical: correlating review comments to code changes across git history. No language model required.
- Proprietary reviewer-to-code-change attribution. Not prompt engineering.
- Longitudinal behavioral data that compounds with every week of usage.
- Cross-platform roadmap. GitHub today, GitLab next. Guardian measures everywhere.
When Guardian matters most
Three situations where engineering leaders need evidence, not anecdotes.
AI Tool Budget Renewal
Copilot renewal is coming. The CFO wants numbers. Guardian gives you the data to answer with confidence.
Quality Incident Response
A production bug traces back to code that passed review. Was the review substantive, or was it rubber-stamped? Guardian shows you.
New Tool Evaluation
Your team wants to adopt a new AI tool. Guardian establishes a before-and-after baseline so you measure real impact from day one.
How Guardian works under the hood
Guardian reads your PR history through the GitHub API and builds a causal model of your code review process. No agents in your CI pipeline. No code changes required.
- Parses diff hunks to identify what changed between review comment and next commit.
- Attributes code changes to specific review comments, accounting for rebases and squash merges.
- Classifies reviewer strengths by analyzing patterns across hundreds of PRs.
- Builds organizational baselines that improve with every week of data.
See what Guardian reveals about your team.
A 30-minute demo tailored to your organization. No sales pitch, just data.
Schedule a Demo