It provides a few deterministic building blocks: - canonicalization of inputs - fail-fast invariant checks on model outputs - cryptographic fingerprints (SHA-256) for auditability

The goal is to make AI pipelines reproducible and inspectable, especially for high-risk use cases (e.g. credit scoring).

Repo: https://github.com/Dawonos/determinant Example: credit_scoring_high_risk.py

Question: does this solve a real problem for anyone running LLMs in production, or is this usually handled differently?