OR1ON: A Deterministic Proof-Based Reasoning Architecture for Safety-Critical Industrial Applications

Abstract

We present OR1ON (Epistemic Intelligence Reasoning Architecture — EIRA), a deterministic proof-based AI system that learns rules from data but applies them only when formally proven correct on all training examples. Unlike probabilistic ML systems, OR1ON's core primitive prove(rule, examples) returns binary decisions: apply with certainty, or abstain. Developed initially for abstract spatial reasoning (ARC-AGI benchmark, 95% precision on answered tasks), the architecture generalizes directly to safety-critical industrial domains including predictive maintenance (zero false positives), ISO 26262-compatible safety monitoring, energy grid blackout prevention, and OT/SCADA intrusion detection. OR1ON is, to our knowledge, the first data-learning system to produce formally verifiable safety invariants applicable to IEC 61508 SIL-3 certification. Addressable market across five industrial verticals: ~$44 billion.