AI Reasons. Code Proves. Evidence Explains.
Quantitative trading models and execution systems silently decay through sign inversions, disguised losses, and double-counted fees. AuditVector orchestrates 6 Google ADK agents to autonomously investigate code ASTs, extract claims, reconstruct bottom-up deterministic FIFO lot matching, and formulate verified code patches tested in an isolated sandbox ($21,960.00 → $0.00) with strict human authorization guards.
IntegrityLab Alpha
Synthetic backtester containing 4 critical flaws: PnL sign inversion, disguised positive return on loss, fee double-deduction, and configuration fee drift ($44,140.00 capital at risk).
IntegrityLab Control
Mathematically sound quantitative baseline demonstrating that AuditVector verifies clean systems with zero hallucinations and dynamically skips remediation when variance is $0.00.
AI-BIP Quantitative Engine
Real-world multi-token momentum engine. Proves $16,286.24 discrepancy (a 30.0484% erosion of reported $54,200.00 profit) against canonical transaction fills.