Rujuvu.ai verifies that an engineer actually understands each AI-generated change — and blocks the merge until they can explain it. Fewer AI-driven incidents, review you can trust, an audit trail you'll need.
Onboarding design partners now · Works with GitHub & GitLab
Your team is merging more code than ever — and no one is checking whether they understand it.
Built for teams where AI writes most of the code
Rujuvu.ai is designed for engineering teams that ship AI-generated code and can't afford to lose understanding of their own systems.
Analyzes every AI-assisted diff and asks the few questions that matter for that change — invariants, failure modes, complexity, security.
Scores understanding from behavior, not self-reporting: files inspected, tests run, whether generated code was edited or accepted verbatim.
Runs as a required check next to CI. Below your team's threshold, the PR stays blocked and Rujuvu.ai teaches until the gap closes.
Rujuvu.ai sits in your PR flow like CI. Instead of checking whether the code passes, it checks whether the engineer can explain it.
Install the GitHub or GitLab app, set an understanding threshold per repo, and mark which paths are high-risk (payments, auth, migrations).
Rujuvu.ai detects generated code, analyzes the diff, and asks targeted questions.
Answers are evaluated against the actual code, combined with behavioral signals from the review session.
Below threshold, the merge stays blocked and Rujuvu.ai explains — adapting to what this engineer already knows — until they close the gap.
Linters, Copilot, CodeRabbit, and human review all ask the same question. Rujuvu.ai asks the one they don't.
Every answered question sharpens a living map of what each engineer verifiably knows. Explanations adapt to it — distributed locking is explained differently to someone who's shipped it. High-risk changes from engineers with gaps get stricter gates automatically.
Engineer profile · verified knowledge
Each merge produces a sealed attestation: who understood what, verified how, and when. As regulation catches up with AI-generated code, you'll already have the paper trail — for SOC 2 evidence, incident reviews, and compliance regimes that will soon demand exactly this.
Reverts, hotfixes, and incidents feed back into the model, so understanding scores learn to predict real defect risk. Over time you learn things like which knowledge gaps actually cause production incidents — data that compounds and belongs to you alone.
Org insights · last 90 days
Rujuvu.ai exposes its assessment engine over MCP, so any AI client routes through the same accountability layer.
"An engineer should never merge code they cannot explain."
Code generation is nearly free. Understanding is now the scarce resource — and the next generation of AI dev tools will compete on how much understanding they create, not how much code they generate.
Code never leaves your VPC in self-hosted mode. Encryption at rest and in transit; SSO and role-based access from day one.
See where AI is masking knowledge gaps across teams before those gaps become incidents. Turn AI adoption into a measured capability.
A required check in GitHub/GitLab, not another tool to open. Thresholds, question depth, and exemptions are configurable per repo and path.
Drop your work email and we'll set up a walkthrough on your codebase. Design partners get free access and a direct line to the roadmap.
For teams where AI writes a meaningful share of production code