The intelligence
behind NUVC.

We present NuScore v5.4, a production scoring engine for pre-seed startup pitch decks that combines an LLM-as-judge architecture with deterministic rule calibration and 42 engineered ML features. Calibrated on 610+ labeled VC investment decisions across four independent sources, NuScore achieves stage-adjusted AU benchmarking and reports raise-probability with confidence intervals. We document the cross-check protocol that resolves LLM/rule disagreement via 70/30 blending and the score waterfall decomposition that exposes ranked signal contributions for explainability.

AI scoring systems used in venture capital evaluation can perpetuate or amplify funding biases. We present the Arendt governance framework — a three-layer fairness system combining (1) statistical bias detection across founder demographics, (2) deterministic ethical guardrails that exclude protected attributes from scoring inputs, and (3) LLM-driven adversarial review. We document the score appeal protocol, automated quarterly bias audits, and the outcome accountability loop that recalibrates the model when score-to-funding correlation drifts.

We present a multi-agent orchestration architecture deployed in production for venture capital intelligence. Eight named pipeline agents (Mia, Olivia, Charlotte, Ava, Isla, Sophia, Amelia, Grace) execute deck extraction, scoring, integrity checking, enrichment, matching, benchmarking, synthesis, and feedback collection in parallel. Underneath, thirteen named intelligence layers (Aristotle, Laozi, Marcus Aurelius, Da Vinci, Sunzi, Seneca, Galileo, Epictetus, Zhuge Liang, Newton, Hypatia, Darwin, Arendt) compound the analysis with audience-specific lens weighting, thesis matching, batch screening, portfolio fit, macro context, and governance oversight. We document the cross-agent dependency graph, the parallelism protocol, and the latency profile achieving sub-60-second end-to-end pitch deck analysis.