Rhinalx is a local-first scientific decision intelligence system that preserves the reasoning behind research decisions, not just the records themselves. Laboratory notebooks capture what changed—a protocol, dose, antibody, or cohort—but rarely preserve why those decisions were made. As projects span months or years, that reasoning becomes scattered across notebooks, meeting notes, chats, and researchers' memories. Rhinalx automatically reconstructs the rationale behind scientific decisions by linking protocols, observations, meeting notes, and experimental records into a cited reasoning chain. It detects decisions with missing explanations, prompts researchers to capture the rationale before it is forgotten, and retrieves previous decisions to prevent repeating failed experiments. The system is designed around local-first AI, allowing unpublished research data to remain entirely on the researcher's machine. Every answer is grounded in traceable source evidence; if sufficient evidence is unavailable, Rhinalx explicitly states that rather than generating unsupported conclusions. By preserving institutional scientific knowledge instead of only experimental records, Rhinalx helps research teams maintain reproducibility, defend protocol decisions, onboard new researchers faster, and reduce the loss of valuable scientific reasoning over time.
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