James Hu

Bayleaf is an AI-assisted decision gate for genomics quality control. The context needed to trust a run is usually scattered across tools that do not talk to each other. Bayleaf brings that context together and follows the run end to end, from composing the pipeline to reviewing the results and preserving a record of why each decision was made. Operators build pipelines as graphs of typed tool cards that compile to runnable Nextflow. Claude can help author a step using a vetted, cited catalog, but pipeline design and execution remain separate and approval-gated. When results come back, deterministic rules assign each sample a verdict: proceed, hold, rerun, or escalate. Each finding is tied to the metric and runbook threshold that triggered it. Missing or unmeasured QC is treated as a signal, not a silent pass. Claude explains decisions and answers questions with citations, helping the operator move faster. But the verdict always comes from the rules, never the model. Underneath, an append-only event log and content-hashed artifacts make every decision reproducible. The log is the source of truth, while the database is a rebuildable projection. Bayleaf is a decision-support tool, not a clinical system. Thresholds are configurable policy, and confidence values are heuristics rather than calibrated probabilities.