Investment Portfolio

PitchScorecard Method™ · Signal Compression v1 · Algorithm v4

Total Deals

Invest

Watch

Pass

2026 MACRO INTELLIGENCE FEED — NARRATIVE LAYER (non-binding)

🤖 AI Revolution

AI startups = 45% of 2024 unicorns. Vibe-coding (Replit, Bolt, Lovable) compresses dev timelines but lowers replication barriers. AI-native moat = table stakes.

🌍 Geopolitical

Supply chain fragmentation. Talent visa restrictions. US-China tech decoupling. Nearshoring trend accelerating. Monitor regulatory exposure.

📱 Attention Economy

TikTok, Reels, YouTube Shorts dominate GTM. Virality is now a product feature. B2C without social hook = significant headwind.

💹 Economic

Rates tightening. Burn multiple scrutiny at peak. Capital efficiency demands: burn <1.5×, LTV:CAC ≥3×. Rule of 40 back in fashion.

Deal Analyzer

PitchScorecard Method™ · Signal Compression v1 · KillSwitch Engine v1

Document Ingestion Engine v1

Upload a PDF pitch deck — engine extracts signals automatically

Company Name *

Sector

Stage

Funding Ask ($)

Drop PDF pitch deck here

or click to browse · PDF, DOC, DOCX supported

Select File

DEAL INFORMATION

Company Name *

Sector

Stage

Geography

Funding Ask ($)

Current ARR ($)

Founder Pre-Money Valuation ($) (optional)

SIGNAL COMPRESSION V1 — 5 CORE SIGNALS

Market + Team + Traction = 75% of decision · Hover cards for tips · ● line = ecosystem avg (65)

UNIT ECONOMICS — KILL SWITCH INPUTS

Used by KillSwitch Engine v1 for contradiction detection. Leave 0 if unknown.

LTV ($)

CAC ($)

ARR ($)

MoM Growth (%)

Monthly Churn (%)

Burn Multiple

MACRO NARRATIVE LAYER (non-binding context)

Informs Investment Memo narrative only — does NOT alter core score (Algorithm v3 fix).

🤖 AI Tailwind

🌍 Geopolitical Risk

📱 Social / Virality

💹 Economic Climate

ADVANCED SIGNALS — MODIFIER LAYER

Scored 0–100. Used in Bill Payne calculation and Investment Memo. Do NOT affect core score.

🏰 Competitive Moat

🌱 ESG / Social Impact

🚀 Exit Potential

📈 Scalability

🤖 AI Readiness

⚡ Vibe Coding Resilience

BILL PAYNE VALUATION CALIBRATION

Set regional median for accurate pre-money estimation.

Regional Median ($M)

Pre-Seed Benchmark ($M)

Seed Benchmark ($M)

No Analysis Yet

Run an analysis to see results here, or load demo data to explore Lucidata and Kidzu.

Deal Comparison

Side-by-side · Up to 6 deals · All engines applied

PitchScorecard Method™

4-Layer Architecture

From pitch to decision — using investor-grade signal detection.

Layer 1

TRUST

Score + Decision + Valuation

Layer 2

CORE

5 Signal Scores

Layer 3

ADVANCED

Radar + Flags + Macro

Layer 4

PRO

Valuation + IRR + Memo

Signal Compression V1

Ruthless prioritization: Market + Team + Traction = 75% of decision. All other factors move to Modifier/Insights layer.

Signal	Weight	What It Measures
🌍 Market	30%	TAM/SAM/SOM, growth rate, urgency, sector timing
👥 Team	25%	Domain expertise, execution history, completeness, commitment
📈 Traction	20%	Revenue, users, CAC/LTV, retention, contracts
⚡ Product	15%	UVP clarity, defensibility, IP, network effects
💰 Biz Model	10%	Monetization, unit economics, scalability, burn

KillSwitch Engine V1

Hard rules that override scores. Real investors don't just average — they eliminate deals.

🔴Weak Team (<40): No execution confidence. VCs will not deploy capital without team conviction.

🔴No Traction (non-pre-seed): Market has not validated the product.

🔴Broken Unit Economics (LTV:CAC <2×): Unsustainable acquisition model.

🔴No Defensibility in Large Market: Commodity startup — easy to copy.

Score Calibration V1 + Confidence Engine V1

Prevents AI score inflation. Forces conservative, realistic scoring.

if (traction < 30 && score > 70) → cap at 65

if (team < 50 && score > 75) → cap at 60

score -= killFlags × 8 + highFlags × 3

score += positiveFlags × 1.5

if (score > 85) → compress: 80 + (excess × 0.5)

78+

INVEST

60–77

WATCH

<60

PASS

Bill Payne Valuation — Promoted to Primary Output

Gold standard for pre-revenue startup valuation. Displayed prominently in results.

payneSum = Σ(factor_score / 70 × factor_weight)

preMoneyVal = regionalMedian × payneSum

// 70 = average score for average company

// Weights: Team 25%, Market 20%, Product 18%, BizModel 15%, Moat 11.5%, ESG 5.5%, Traction 5%

Help & User Guide

Getting Started

1Load Demo Data — Click "Load Demo Data" to see Lucidata and Kidzu pre-analyzed. Click any card to view full analysis.
2Choose Input Method — Manual Score (most accurate), Paste Pitch Text (NLP extraction), or Quick Score (5-slider rapid).
3Score 5 Core Signals — Rate each 0–100. Add rationale for higher confidence scores.
4Enter Unit Economics — LTV, CAC, ARR, growth, churn, burn multiple. These trigger KillSwitch Engine v1.
5Run Analysis — Get INVEST/WATCH/PASS, red flags, green lights, Bill Payne valuation, Investment Memo, and PDF export.

Scoring Guide

Score	Interpretation	Example
80–100	Exceptional — top-quartile	Team: Ex-Google founder, 2 exits, full team
65–79	Above average — strong evidence	Market: $500M TAM with 3rd-party validation
50–64	Average — mixed signals	Traction: Some users but no revenue
35–49	Below average — weak evidence	Biz model: Unclear monetization
0–34	Red flag — significant risk	Team: Solo founder, no domain expertise

Data Privacy

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✓localStorage Only — Portfolio stored locally. Clear browser data to reset.

✓PDF Generated Locally — jsPDF runs in-browser. No upload, no cloud.

✓No Tracking — No analytics, no cookies, no accounts required.

Disclaimer

PitchScorecard.com is an analytical tool designed to assist investment decision-making. It does not constitute financial advice, investment advice, or a recommendation to buy or sell any security. All scores and valuations are estimates based on input data. Always conduct thorough due diligence before making investment decisions.

About PitchScorecard

Evaluate. Compare. Invest.

Research & Development

PitchScorecard was developed by Dr. Beza B. Lefebo, a distinguished expert in the fields of cybersecurity and artificial intelligence. Dr. Lefebo holds a Doctor of Engineering in Engineering Management and Systems Engineering, with a focus on Analytics and Machine Learning AI, from George Washington University.

His doctoral research involved the development of a novel algorithm designed to predict and detect Distributed Denial-of-Service (DDoS) cyberattacks targeting critical U.S. smart grid power infrastructure. Beyond his technical research, Dr. Lefebo is a published author, dedicated educator, and a sought-after advisor to industry leaders.

Disclaimer & Intellectual Property

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Notice

PitchScorecard provides analytical insights and is not intended to serve as financial advice. Users should conduct their own due diligence.

Algorithm v3 Signal Compression v1