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How deployment failures hide in pull requests, and how we build Proofline to find them. RSS

  1. A one-line scale-up can exhaust your database

    Raising a service's task count is a correct change in the diff. Whether production can absorb it depends on numbers that live in the environment, outside the repository.

    Capacity overrunsThe Proofline team3 min read

  2. Review AI-generated code

    To review AI-generated code, Proofline checks it against your production environment, from the terminal or on the pull request. Coding agents write correct-looking code without seeing that environment.

    Deployment failuresThe Proofline team1 min read

  3. Code review audit trail

    Proofline keeps a code review audit trail with one record for each review, and the database refuses updates to a review's findings and evidence bundle. When a deployment goes wrong, the record shows what anyone knew before the change shipped.

    CompanyThe Proofline team1 min read

  4. AI code review for AWS

    With AI code review for AWS, Proofline checks each pull request against the account it deploys to. It reads your services, databases, images, and load balancers through a read-only role, then explains how the change could fail there.

    Deployment failuresThe Proofline team2 min read

  5. BYOK AI code review

    Proofline runs AI code review on your own model provider key, with your choice of model for each review stage. The Free plan costs $0 per seat, and your provider bills inference.

    CompanyThe Proofline team2 min read

  6. Cloud Run deployment review

    Proofline reads the settings a Cloud Run service runs with today and checks each pull request against them. Those settings live outside the repository: environment variables, traffic splits, and scaling limits.

    Deployment failuresThe Proofline team1 min read

  7. Container image review

    Proofline matches the platforms in a container image to the declared runtime, and follows images by digest from the build to the running service. The image a pull request builds has to match what will run it.

    Broken dependenciesThe Proofline team1 min read

  8. Full control over your code review

    Proofline settings decide when a review runs, what it reads, what it may block, and what it costs. The audit log records every settings change.

    CompanyThe Proofline team2 min read

  9. The most customizable AI code review

    Customize code review by telling the reviewer what matters in your organization, each environment, and each repository. Settings cascade from the organization down, and a repository can override what it needs.

    CompanyThe Proofline team2 min read

  10. Database migration review

    Proofline reviews a database migration against the version still running, as well as the code in the pull request. The current release keeps using the database while the migration runs.

    Breaking migrationsThe Proofline team2 min read

  11. Deployment gates

    A merge gate holds a pull request, and a deployment gate holds a deployment, until its review is complete and no blocking finding is open. A fixed policy that you set makes each decision, never the model.

    Deployment failuresThe Proofline team2 min read

  12. Deployment risk analysis

    Proofline analyzes the deployment risk of every pull request across 18 risk domains. It records which domains the change touched, which it skipped, and why.

    Deployment failuresThe Proofline team2 min read

  13. Free AI code review agent

    Run Proofline's AI code review agent free on the Free plan. You pay $0 per seat, bring your own model key, and your provider bills inference. Evidence connections are read-only.

    CompanyThe Proofline team2 min read

  14. GitHub Actions workflow review

    Proofline reviews GitHub Actions workflow changes under .github/workflows/ for their build and deployment effects, and checks that every external action is pinned. A workflow file decides what ships and where.

    Deployment failuresThe Proofline team1 min read

  15. AI code review for Google Cloud

    Proofline runs AI code review against the Google Cloud project each pull request deploys to. It needs no stored Google key to read your services, databases, images, and builds.

    Deployment failuresThe Proofline team2 min read

  16. Kubernetes deployment review

    In a Kubernetes deployment review, Proofline compares the manifests in a pull request with the objects your cluster already runs. A valid manifest can still fail in the cluster it lands in.

    Deployment failuresThe Proofline team2 min read

  17. Low-noise AI code review

    Low-noise AI code review means Proofline publishes a finding only when evidence supports it, says it once, and records the rest without posting. A reviewer that comments on everything trains people to ignore it.

    CompanyThe Proofline team2 min read

  18. Secure AI code review

    Proofline limits what its AI code review model can reach, so a manipulated model cannot widen its own access. The reviewer reads code from people you do not control, and text that may try to steer it.

    Security incidentsThe Proofline team2 min read

  19. Terraform pull request review

    Proofline reads your Terraform state and the pull request's plan beside the diff, so a review can say what the change does to resources that already exist. The diff alone shows only the configuration you changed.

    Deployment failuresThe Proofline team2 min read

  20. Vercel and Cloudflare Workers review

    Proofline reads routing and rollout state from Vercel and Cloudflare Workers, so a pull request review can account for what is already deployed. Edge platforms keep that state outside the repository.

    Deployment failuresThe Proofline team1 min read