Aviation Safety Evidence Engine

Aviation Safety Evidence Engine Aviation investigators synthesize long, conflicting PDFs where untraceable AI summaries fail. Our dashboard lets analysts click a component on an aircraft schematic to build a bounded evidence pack and generate a verified briefing: documented findings, unknowns, and next checks. Unanalyzed parts stay gray so safety is never implied. Tech Stack & Split Evolus prepares data via REST (Document to data: 52.8 credits/32.3s; Summary: 11.72 credits/5.2s), creating an unmerged overview labeled "generated, not verified." Gemma 4 31B IT (AMD Developer Cloud) provides open-weight reasoning suitable for private deployment, while deterministic code verifies every claim. The 7-Part Harness Code governs what reaches the screen: (1) Bounded packs with leakage guards and contradiction searches; (2) Strict prompts requiring passage IDs and verbatim quotes; (3) Injected JSON schemas with one repair retry; (4) A deterministic substring verifier that flags—never hides—failed quotes as "needs review"; (5) Provenance pulled strictly from stored metadata; (6) No model calls on empty packs; and (7) Full audit logs for a replay-only demo. Results & Limits Tested on an Alaska Airlines 737 gear collapse (NTSB DCA23FA417, cause withheld) across 0–44 passages (up to 15.7k tokens). Showing the schema raised first-try parsing from 0/3 to 3/3, and the verifier passed 13/16 citations. Runs identified the fracture mechanism but missed the root cause (overhaul grinding heat damage). Built with Python, FastAPI, Pydantic, SQLite FTS, pypdf, and Docker, it ensures quote traceability, not interpretive accuracy.