Traditional LLMs feel unpredictable because nothing behind the output is stable or inspectable. In a black-box system, you can’t see what data shaped the answer, which adapter was used, or whether the model quietly changed since yesterday. OpenLedger replaces that uncertainty with deterministic, attributable inference. Every response carries its own origin story: data source, adapter stack, model lineage, execution proof, and timestamped verification.