Investor Intelligence Tool — Free

Know the Difference Between Real AI and AI Washing

30 questions across 8 dimensions. Designed for VCs, angels, and family offices evaluating AI-first companies. Built on enterprise AI research—not hype.

30scored questions
8investment dimensions
~12minutes to complete
100point composite score
Tell us about the company you are evaluating
Used only in your printed report. Nothing is stored or transmitted.
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Data Moat
Does the company own proprietary data that improves the model over time? Or does it just call the same APIs competitors use?
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Model Independence
Companies built entirely on top of a single foundation model API have zero technical moat. Evaluate what they actually own.
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Unit Economics
Inference costs are real and scaling. Many AI products have inverted gross margins. This is the CFO question most boards aren't asking.
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Team Depth
Research pedigree matters—but so does commercial execution. A team that can only build the model, not sell and deploy it, is half a team.
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Defensibility
Network effects and switching costs degrade quickly in AI. What makes customers harder to replace in year 3 than year 1?
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Customer Evidence
Pilots are free marketing. Enterprise contracts with real SLAs, genuine NRR, and named references are the signal.
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Market Timing
AI-native vs. AI-washed. Is the company solving a problem that genuinely requires AI, or adding AI to something that was already solved?
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Regulatory Risk
EU AI Act, HIPAA, financial regulations, and sector-specific compliance create asymmetric liability for under-prepared AI companies.

Built on frameworks from Sequoia Capital AI Thesis 2024, a16z AI Stack research, McKinsey AI Value Survey 2024, and enterprise deployment data from 50+ Fortune 500 AI projects.
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Evaluation Progress 0 / 30
Section 01 — Executive Summary
0 /100
Composite Score
Evaluating…

Eight-Dimension Competency Profile
Evaluated Company
Company
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Founders
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Stage
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Check Size
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Section 02 — Dimension Performance Analysis
■ Strong (70%+) ■ Moderate (45–69%) ■ Weak (<45%)
Section 03 — Return Potential Analysis
Investment Verdict Score Range Return Potential Recommended Posture
Return ranges are probability-weighted estimates, not guarantees. They reflect structural characteristics that historically correlate with venture-scale outcomes. Individual results depend on market timing, team execution, and external factors. This report is a diligence aid, not investment advice. Framework by Arjun Jaggi — arjunjaggi.com
Section 04 — Risk Signals & Investment Strengths
Section 05 — Dimension-by-Dimension Findings
Section 06 — Recommended Next Steps
Appendix — Methodology & Framework Basis

The AI Investment Scorecard evaluates companies across eight structural dimensions identified through analysis of enterprise AI deployments and venture-backed AI companies. The framework draws on Sequoia Capital AI Thesis 2024, a16z AI Stack research, McKinsey Global Institute AI Value Survey 2024 (50+ Fortune 500 deployments), NIST AI Risk Management Framework 1.0, EU AI Act compliance requirements, and primary research from 200+ enterprise AI evaluations. Each dimension is independently scored and combined into a weighted composite. Weights reflect empirical correlation with durable investment outcomes rather than equal weighting.

AJ
Arjun Jaggi
Enterprise AI Strategist · Innovation & Growth Executive
Santa Monica, California · arjunjaggi.com
11 Patents Granted
USPTO & International — AI Systems, Security, Enterprise Intelligence
20+ Peer-Reviewed Publications
IEEE & Scopus — Agentic AI, Machine Learning, Applied Intelligence
$300M+ Strategic Deals Closed
AI transformation engagements with Fortune 500 C-suites
7× International Awards
AI Innovation & Research Recognition, 2018–present
2 Books Co-Authored
Applied AI & Enterprise Innovation
Fortune 500 Advisory Portfolio
CVS, Thermo Fisher, Whirlpool, 3M & others
Sectors: Healthcare · Manufacturing · Finance · Retail · Federal Government · Oil & Gas · Life Sciences · Entertainment
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AI Investment Scorecard — Free Tool — arjunjaggi.com/tools/ai-investment-scorecard.html Framework built on Sequoia AI Thesis 2024 · a16z AI Stack · McKinsey AI Value Survey 2024 · NIST AI RMF 1.0