Know which deploys will fail before you merge.
Koalr scores every PR 0–100 using 36 research-validated signals before it ships — explains exactly why it's risky, and suggests how to fix it.
Koalr reads the envelope, not the letter.
We connect to GitHub to read PR metadata — file paths, sizes, timing, and review patterns. Your source code, commit content, and PR descriptions are never read, stored, or transmitted. Period.
See exactly what we accessDeploy Risk Score
87
HIGH RISKPR #2847
feat/payments-v2
+1,240 / -340 lines
Up and running in minutes.
Connect your GitHub org
No agents. No YAML. No instrumentation. OAuth in, and you're done.
Koalr analyzes every PR
All 36 signals run automatically on every pull request — before anyone clicks merge.
Get a risk score
Red, amber, or green. Reason codes. Posted as a GitHub Check Run. Every time.
36 signals. One score. Before you ship.
Author familiarity
Someone making their first commit to payments-service is statistically higher risk than the engineer who owns it.
Concurrent merges
Multiple PRs landing to the same service in a short window compound failure probability.
Review depth
A rubber-stamp approval — zero comments, instant LGTM — correlates with missed defects. We measure it.
Blast radius
If this service fails, which teams get paged? Which customer flows go down? Mapped before you deploy.
...and 32 more, all research-validated. Read the research →
The research behind the score
Deployment Intelligence
65,000 words on why deploys fail, how to predict them, and what elite engineering teams do differently. Free. No paywall. No credit card.
The deploy intelligence platform
Predict. Explain. Fix. Learn.
Every other tool tells you what happened. Koalr stops it from happening — before you merge.
Score every PR before merge
36 research-validated signals — coverage delta, blast radius, DDL migrations, author familiarity, and more. Score 0–100. Every time.
Plain-English AI explanations
Not just a number. "This PR scores 87 because of a DDL migration with no rollback, zero test coverage on changed files, and no reviews from anyone familiar with payments-service."
Generate the actual fix
Rollback SQL. CI workflow YAML. Reviewer suggestions by name. CODEOWNERS diffs. Not advice — artifacts ready to copy-paste or post as a GitHub suggestion.
Get smarter with every deploy
Outcome data (incidents, rollbacks, deploy failures) feeds back into the model. Koalr learns which signals actually predict incidents for your org.
No competitor does any of this.
LinearB has $84M raised and 6 years. They still don't predict, explain, fix, or refine risk signals from deployment outcomes.
| Capability | Koalr | LinearB | Swarmia | Jellyfish |
|---|---|---|---|---|
| Pre-merge deploy risk prediction | — | — | — | |
| AI explanation of why it’s risky | — | — | — | |
| Auto-generated fix artifacts (SQL, YAML, diffs) | — | — | — | |
| Outcome learning (per-org signal tuning) | — | — | — | |
| GitHub Check Run to block risky merges | — | — | — | |
| CODEOWNERS compliance enforcement | — | — | — | |
| Audit-ready compliance reports (CMMC, SOC 2, ISO 27001, PCI-DSS, FedRAMP) | — | — | — | |
| DORA metrics | ||||
| PR analytics |
Everything your team needs to ship confidently.
Track what matters
Deployment frequency, lead time, MTTR, and change failure rate — automatically calculated from your GitHub data.
Understand why it's risky
Plain-English AI explanations for every high-risk score — not just a number, but exactly which signals fired and what to do about it.
Ask anything about your engineering data
"Why is our change failure rate trending up?" Context-aware AI answers backed by your real incident, PR, and deployment data.
Know who's actually reviewing
Reviewer expertise scoring, CODEOWNERS sync, and rubber-stamp detection — so approvals mean something.
Works with the tools you already use
Find out which of your deploys are high risk.
Free tier, 5 seats. Connects to GitHub in minutes.
Frequently asked questions
Everything you need to know about Koalr and deploy risk prediction.
Still have questions? Email us →