Rejourney is a lightweight, self‑hostable observability couldn't be simpler. Rashid, a sophomore at UT Austin, built the tool after losing 340 users from his campus app due to onboarding issues. The SDK lands on Web JS, Swift, or React Native, and developers tag critical conversion events like sign‑ups orбал purchases. The system records full user sessions, aggregates touch, scroll, and API data, then clusters similar journeys. An LLM—Gemini by default—analyzes frames to flag negative trends and outputs markdown fixes, optionally pulling code from an attached GitHub repo. With 2.5M daily recordings and a 30% onboarding lift in one case, Rejourney scales cost‑effectively, breaking even with just three paid users.

Privacy is baked in: after a 7‑day retention window, recordings are anonymized and aggregated into dashboards similar to Firebase’s analytics Centros. The tool supports web and mobile, providing session replay, heatmaps, crash context, and geographic cohorts to isolate revenue‑leak signals.

Developers can log domain events to protect states—signup, checkout, subscription, or renewal—and compare cohorts against healthy baselines. The result is a ranked candidate leak with evidence ready for human verification and quick fixes.