LLM Eval Scorecard

Developers discover runaway LLM API costs only at month-end when the bill arrives because existing provider dashboards lack proactive anomaly detection

ML teams cannot trace which source data ended up in which model, making it impossible to respond to data recall requests or debug production incidents

ML engineers lose track of which dataset version produced which model, making reproducibility and debugging nearly impossible