RePro

RePro is an incident remediation platform built for the highest-friction moment in software delivery: when something breaks in production and the team has to move fast without making the situation worse. Today that process is fragmented across logs, screenshots, Slack threads, GitHub comments, local repro steps, and manual review. RePro turns that chaos into a single verified-fix workflow. It ingests incident evidence, identifies the likely root cause, reproduces the failure in an isolated sandbox, generates and tests a patch, and stops at an explicit human approval gate before opening a pull request. Every step is captured in a live run trace with receipts, verification data, approval records, and reproducibility evidence. The problem it solves is not “writing code faster.” It solves slow, expensive, high-risk incident response. When systems fail, engineering teams burn senior time on triage, context gathering, reproduction, validation, and communication. RePro reduces MTTR, cuts operational overhead, and creates a repeatable path from incident report to safe remediation. The key value is trust: sandboxed execution, repo allowlists, policy guardrails, CI verification, and audit-ready receipts make AI-assisted fixes usable in real organizations, not just demos. What makes it different is that it closes the loop end to end. Most AI developer tools generate suggestions and leave the hard part to humans. RePro starts with messy real-world evidence and carries the workflow through diagnosis, reproduction, patching, verification, approval, and PR creation. That makes it feel less like a chatbot and more like an operational system for software reliability. If you want a shorter version, use this: RePro turns production incidents into verified, approval-gated pull requests. Instead of forcing engineers to manually triage evidence, reproduce failures, test fixes, and document everything under pressure, it runs the entire remediation workflow in one place: incident intake, root-cause analysis, sandbox reproduction, patch generation, verification, approval, and receipts. The result is faster recovery, lower operational risk, and a practical way for teams to use AI in incident response without losing control, traceability, or accountability.