RaptorCI focuses on risk, not output. While most tools generate comments, rules, or pass/fail checks, they don’t show what could actually break. RaptorCI analyses pull requests to identify high-impact changes, explains their potential impact, and gives a clear signal of how safe a change is to ship. Built after seeing risky changes repeatedly slip through review in production systems, it’s already being used by teams reviewing real pull requests and iterating quickly based on feedback.
Hey everyone 👋
I’m Jordan, founder of RaptorCI.
I built this after repeatedly seeing the same issue while working on production systems — changes would pass code review and CI, but still cause problems in production. Reviews focus on correctness, CI gives pass/fail, but neither answers “what could this actually break?”
RaptorCI is my attempt to solve that. It analyses pull requests and highlights the changes that actually matter — things like sensitive code paths, config changes, or missing coverage — and explains their potential impact so teams can make better decisions before merging.
The first version was built and launched in under 2 weeks, and it’s now being used by a few teams reviewing real PRs. I’m iterating quickly based on feedback and trying to keep the signal clear without adding more noise.
Would genuinely love to hear what you think — especially from anyone reviewing code regularly. What’s missing in your current workflow?
For now we review PR manually with humans, how can this help us? Does it replace the work done or make it safe by adding some comments?
Congratulations on the launch! Currently I need NDAs in place to even discuss designs with someone - are there protections / contracts in place that would allow us to use your service?
Something I have been wondering but would to get a wider take: "Do you trust AI in code reviews yet, or still skeptical? What would give you the confidence to rely on it?"
Hey Jordan, excited to try this out! Do you have a support email? Having some billing/activation troubles. Thanks!
How do you decide a change is “high-impact” in practice—are you using repo-specific learning (hotspots, ownership, past incidents), deterministic heuristics (config/auth/billing paths), or LLM judgment—and how do you prevent the risk score from being gamed by small diffs that are actually dangerous?
