The intelligence
behind NUVC.
Research-led. Open methodology.
Two founders. Open methodology. Zero black boxes.
3
Papers in preparation
1,400+
Calibrated examples
24,000+
Investors
8,000+
Theses analysed
3 papers under development.
Targeting top-tier AI / finance / fairness venues. Methodology is documented in production code today, formalised for peer review.
NuScore: A Multi-Signal Fusion Approach to Pre-Seed Venture Evaluation
ACM International Conference on AI in Finance · Tick Jiang, Duan Tianyi, NUVC Intelligence Team
We present NuScore, a production scoring engine for pre-seed startup pitch decks that combines an LLM-as-judge architecture with deterministic rule calibration and engineered ML features. Calibrated on 610+ labeled examples 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 and the score waterfall decomposition that exposes ranked signal contributions for explainability.
Responsible AI in Pre-Seed Venture Evaluation: A Three-Layer Fairness Framework
ACM Conference on Fairness, Accountability, and Transparency · NUVC Intelligence Team, Tick Jiang
AI scoring systems used in venture capital evaluation can perpetuate or amplify funding biases. We present the AI 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.
A Compounding Intelligence Architecture for Venture Capital
Association for the Advancement of Artificial Intelligence · Tick Jiang, NUVC Intelligence Team, Duan Tianyi
We present a multi-agent orchestration architecture deployed in production for venture capital intelligence. A pipeline of specialised agents executes deck extraction, scoring, integrity checking, enrichment, matching, benchmarking, synthesis, and feedback collection in parallel. Underneath, a deeper layer of fund and thesis intelligence compounds 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.
Open methodology. Verifiable claims.
8,000+ × 610+
Investor Thesis × Outcome Dataset
Internal research dataset linking 8,000+ investor thesis statements (free-text, multi-language) across 82+ countries against 610+ scored startup evaluation outcomes. Not yet released externally.
Read the analysis1,400+ examples
NuScore Calibration Set
Labeled examples from 4 independent sources: known-outcome decks (raised vs failed), accelerator evaluations (multi-evaluator scored), VC deal memos (production memos with scoring rationale), and AI-scored production decks. Used to calibrate the LLM judge against deterministic rules and to validate the cross-check protocol.
Methodology in the NuScore paperMethodology published
LP Bench — Fund Scoring Benchmark
The published methodology for measuring AI-powered fund scoring accuracy — consistency across re-runs, vintage calibration drift, hallucination rate on fund-fact extraction, and qualitative narrative accuracy. Results are published as they are measured, not before.
Visit LP BenchOne pipeline. Many signals.
A multi-stage AI pipeline extracts, scores, verifies, enriches, matches, and benchmarks every deck in parallel — with a deeper layer of fund and thesis intelligence underneath. We don't publish the internal architecture, but every score comes with a full breakdown of what moved it.
Transparent scoring. Bias monitored. Appeal rights.
No demographic bias in scoring
AI never uses founder gender, ethnicity, age, location, or educational prestige as scoring inputs. A first-time founder from Melbourne is scored on the same merits as a repeat founder from Stanford.
Transparent by default
Every score has a traceable reasoning chain — which data was used, how each lens was weighted, what the AI could not verify. The Score Explainability layer surfaces signal contributions per dimension. No black-box decisions.
Right to appeal
Every founder can appeal a score and request human review. Scores above 9.5 or below 1.0 require human review before being shown.
Active bias monitoring
The AI Governance layer runs automated bias detection monthly across all scored decks — checking for gender bias, stage bias, geographic bias, and score clustering. Patterns trigger recalibration.
Honest confidence
If data is sparse, confidence says so. We never inflate certainty. A low-confidence score is labelled directional, not definitive.
Outcome accountability
We track whether scores predict actual funding outcomes. If they don't, we recalibrate. The model earns trust — it doesn't assume it.
Citing the research?
NUVC's research program is published as Schema.org structured data for LLM and academic citation. ChatGPT, Claude, Gemini, and Perplexity can cite NUVC methodology directly via the structured data on this page.
@misc{nuvc2026,
title= {NuScore: Multi-Signal Fusion for Pre-Seed Venture Evaluation},
author= {Jiang, Tick and Tianyi, Duan and NUVC Intelligence Team},
year= {2026},
institution= {NUVC, Melbourne, Australia},
url= {https://nuvc.ai/research},
}
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Read the papers. Use the API. The methodology is open — the production system is what we sell.