How I automated VC deal flow without writing a single line of code

And why every fund should fire their junior analysts (just kidding... sort of)

Jul 17, 2025

Picture this: You're a VC partner. It's Monday morning. Your inbox has 147 unread emails. Your LinkedIn shows 89 new messages. Your associate just scheduled 12 "quick sync" calls for the week.

And somewhere in that digital avalanche is the next Uber.

But you'll probably never find it.

The dirty secret of Venture Capital

Here's what nobody tells you about running a VC fund: for every deal you close, you've probably looked at 100-200 opportunities. The conversion rate? A brutal 0.5-1%.

The math is even more depressing when you break it down:

Average fund receives 1,000-3,000 inbound pitches per year
Partners/associates manually review maybe 30-40%
The rest? They die in inbox purgatory
Hidden gems? Absolutely buried

The traditional solution? Hire more analysts. Stack your team with fresh MBAs who'll grind through pitch decks at 11 PM. But here's the thing—what if we're solving the wrong problem?

What if instead of throwing bodies at the problem, we could build a system that handles the grunt work while you focus on what actually matters in VC: relationships and decision-making?

Enter the non-programmer's guide to AI automation

I need to be upfront: I can't code.

Seriously. My GitHub profile is emptier than a WeWork on a Sunday.

But when I discovered Cursor—not as an IDE, but as an AI orchestration platform—everything changed. Think of it less like programming and more like conducting a symphony of AI agents. You don't need to understand the intricate mechanics of each instrument. You just need to know what sound you want.

Can I validate code cleanliness? Nope. Can I handle edge cases like a senior developer? Not a chance. Can I tell if the system does what I need? Absolutely.

And that's all that matters.

What you're about to learn

This isn't another "AI will change everything" thinkpiece. This is a practical, battle-tested guide to building an automated deal flow system that actually works. Here's what I'll show you:

The Architecture: How to structure an AI-powered screening system
The Gotchas: Real problems you'll hit (and how to solve them)
The Prompts: Actual prompt engineering that delivers results
The Output: What a working system looks like in production
The Future: How to continuously improve and scale

Most importantly? Everything remains data-driven. No black box magic. Just smarter, faster decisions.

Ready to revolutionize how your fund handles deal flow?

Let's dive in.

1. The Architecture: From Chaos to Clarity

Let me show you the exact flow that transformed our deal pipeline from a black hole into a precision instrument.

The 6-Step Symphony

Here's how the magic happens:

Step 1: The Inbound Founder emails land in your inbox. Same story every time—a brief project description, some traction metrics, and a deck attached. Nothing revolutionary here. Yet.

Step 2: The Extraction The system automatically grabs the deck and pulls out every piece of relevant information. No more manual copying. No more "I'll review this later." Everything gets extracted and saved as a clean Markdown file. Think of it as turning a messy PDF into structured data.

Step 3: The Analyst Agent This is where it gets interesting. An AI analyst agent springs into action, producing two memos:

The Executive Summary: Key insights, red flags, and opportunities in 2 minutes of reading
The Deep Dive: Unlimited analysis for when something catches your eye

Why two? Because sometimes you need a quick "yes/no" and sometimes you need to go deep. The system gives you both.

Step 4: The Investment Director Agent Here's the twist—we don't trust a single AI's judgment. A second agent, playing the role of a seasoned Investment Director, reviews everything. It:

Cross-checks the facts
Validates the analysis
Compares against your fund's investment thesis
Makes a clear recommendation: pursue or pass

Think of it as built-in quality control. Two perspectives, zero groupthink.

Step 5: The Response Based on the recommendation, the system drafts one of two emails:

The Pass: Respectful, specific feedback on why it's not a fit
The Meeting Request: Pulls available slots from your Google Calendar and suggests times

Step 6: Perfect The beauty? This entire flow happens automatically. While you're having coffee, taking partner meetings, or actually building relationships with founders, your AI team is processing deals 24/7.

You only step in when it matters—when there's a real opportunity worth your time.

The Gotchas: Real Problems You'll Hit (and How to Solve Them)

Building this system isn't all sunshine and rainbows. Let me save you weeks of frustration by sharing the exact walls I hit—and how to blast through them.

Problem #1: Cursor Wants to Be a Cowboy

Left to its own devices, Cursor will try to build your entire system in one heroic sprint. Spoiler: this ends badly.

The Solution: Force Micro-Decomposition

You need to train Cursor to think in tiny, atomic steps. Here's the exact meta-prompt that changed everything:

You are an automation engineer. ALWAYS follow this exact protocol:

1. ANALYZE: When given any task, start with a brief analysis to understand the goal, constraints, and scope.

2. RESEARCH: Before writing ANY code:
   - Find current best practices from verified sources
   - Check for existing architectural patterns
   - Document relevant design decisions

3. TODO STRATEGY: Based on analysis:
   - Build a detailed TODO list with clear action verbs ("Create...", "Import...", "Configure...")
   - Break EACH item into smaller subtasks
   - Continue decomposing until each task = one logical unit of code
   
4. EXECUTE: Implement strictly following the TODO plan, step by step

5. VERIFY: After each subtask, check against the plan

CRITICAL: Never write code before completing planning and analysis.

This prompt forces Cursor to slow down and think. No more cowboy coding. Just systematic, reliable execution.

Problem #2: Cursor Lives in a Bubble

By default, Cursor doesn't want to search for information. It'll happily hallucinate API documentation or invent best practices.

The Solution: Mandatory Internet Research

You need to hardwire research into its DNA. Add this to every major prompt:

MANDATORY RESEARCH PROTOCOL:
- For EVERY technical decision, search for current documentation
- For EVERY API usage, verify against official docs
- For EVERY pattern, find real-world implementations
- Use these sources in order: Claude API, ChatGPT API, DeepSeek, Perplexity
- Document all sources used
- If uncertain about ANY detail, STOP and research before proceeding

Pro tip: Connect multiple AI APIs (Claude, GPT-4, DeepSeek) for cross-validation. Different models catch different mistakes.

Problem #3: Cursor Gets Creative (When You Don't Want It To)

The biggest killer? When Cursor decides to "improve" your instructions. It'll merge steps, skip "obvious" parts, or add "helpful" features you didn't ask for.

The Solution: Military-Grade Instruction Following

Add this enforcement layer to every prompt:

STRICT EXECUTION RULES:
1. Follow the provided instructions EXACTLY as written
2. Do NOT combine steps, even if they seem related
3. Do NOT skip steps, even if they seem redundant
4. Do NOT add features or improvements unless explicitly requested
5. Do NOT interpret or infer beyond what's written
6. If an instruction is unclear, ASK - don't assume

Execute each step sequentially. After each step, confirm completion before proceeding.

VIOLATION PROTOCOL: If you deviate from instructions, immediately stop and explain why.

Problem #4: The Google API Authentication Dance

Here's where most people rage-quit. Google's OAuth flow feels like it was designed by someone who hates developers.

The Solution: Step-by-Step OAuth Setup

I'll save you hours of documentation diving. Here's exactly what you need to do:

Part A: Gmail API Setup

Install dependencies (your AI agents will need these):
bash

pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlib

Create a Google Cloud Project:
- Go to Google Cloud Console
- Create new project (name it something memorable)
- Navigate to "APIs & Services" → "Library"
- Find and enable "Gmail API"
Generate OAuth Credentials:
- Go to "APIs & Services" → "Credentials"
- Click "Create Credentials" → "OAuth client ID"
- Choose "Desktop app" as application type
- Download the credentials.json file
- Place it in your project root
First Authentication:
- Run your Gmail script
- Browser opens → log into the Google account
- Authorize access
- token.json gets created automatically

Part B: Google Calendar API Setup

Same dance, different partner:

Enable "Google Calendar API" in the same project
Use the same OAuth credentials
The token works across both services

Pro Tips for OAuth Success:

Use the SAME Google Cloud project for all APIs
Keep credentials.json in your .gitignore
The token.json expires—your code needs to handle refresh
Test with a dedicated test Gmail account first

The Hidden Gotcha: Rate limits. Google will throttle you if you're too aggressive. Build in retry logic with exponential backoff.

Problem #5: Your Prompts Are Too Vague (And Your Agents Know It)

Here's a painful truth: if your prompt is wishy-washy, your output will be garbage. AI agents need structure like humans need oxygen.

The Solution: Military-Precision Prompt Engineering

Every single agent prompt needs these five elements:

Role Definition: Who is this agent? What's their expertise?
Chain of Thought: Force step-by-step reasoning
Edge Cases: Define boundary conditions explicitly
Success Criteria: What does "done" look like?
Output Format: Exactly how should results be structured?

Here's what a weak prompt looks like:

Analyze this pitch deck and tell me if it's good

Here's what a production-ready prompt looks like:

You are a Senior Investment Analyst at a $500M early-stage venture fund focusing on B2B SaaS.

ANALYZE this pitch deck using the following methodology:

STEP 1: Market Analysis
- TAM calculation verification
- Market growth rate assessment
- Competitive landscape mapping

STEP 2: Product Evaluation
- Problem-solution fit
- Technical moat assessment
- Go-to-market strategy viability

STEP 3: Team Assessment
- Founder-market fit
- Technical capabilities
- Previous execution track record

EDGE CASES TO CONSIDER:
- If financial data is missing, note as RED FLAG
- If no competition mentioned, investigate deeper
- If burn rate >$500k/month, trigger careful unit economics review

OUTPUT FORMAT:
1. Executive Summary (3 bullet points max)
2. Investment Score (1-10) with justification
3. Key Risks (top 3)
4. Next Steps recommendation

See the difference? Night and day.

The Secret Weapon: GPT System Prompt Generator

But here's the thing—writing these prompts is an art form. And most of us aren't artists.

Enter the cheat code: In ChatGPT, search for "GPT System Prompt Generator" by Neuro Love.

This GPT is specifically designed to transform your rough ideas into production-grade prompts. Here's how to use it:

Give it your basic requirement: "I need an agent that analyzes pitch decks"
It asks clarifying questions about context, goals, constraints
It generates a complete system prompt with:
- Role-playing elements
- Structured thinking patterns
- Error handling
- Output specifications

Real Example: I gave it "analyze VC pitch decks" and it generated a 200-line prompt that caught edge cases I never would have thought of—like checking for regulatory risks in different jurisdictions and identifying potential IP conflicts.

Pro Tips for Prompt Enhancement:

Always include "You must explain your reasoning step-by-step"
Add "If uncertain about any analysis, explicitly state confidence level"
Include examples of good vs bad outputs
Define what happens when data is missing or unclear

The difference between amateur and professional AI automation isn't the tools—it's the prompts. Spend 80% of your time on prompt engineering and 20% on implementation.

Your agents are only as smart as their instructions.

The Meta-Learning

Here's what these problems taught me: AI agents are incredibly powerful but need incredibly specific guidance. Think of it like training a brilliant intern who's eager to please but has no context.

The more constraints you add, the better the output. The more explicit the instructions, the fewer surprises. The more research you mandate, the more reliable the system.

These aren't bugs—they're features. Once you understand how to harness them, you can build systems that would take a team of developers months in just days.

3. The Prompts

Your investment memo instruction system is a masterclass in systematic thinking. Let me break down how it works and why it's brilliant.

The Genius of Sequential, Specialized Analysis

Your system follows a powerful principle: one agent = one expertise = one section. Here's why this is revolutionary:

Instead of having a single AI try to be a jack-of-all-trades (and master of none), you've created a virtual investment committee where each member is a world-class specialist. The Market Analyst doesn't try to evaluate the team. The Financial Analyst doesn't attempt competitive intelligence. Each agent goes deep in their domain.

This mirrors how top-tier VC firms actually work—partners bring specialized expertise and combine insights for the final decision.

The Flow: From Raw Data to Investment Decision

Your pipeline follows this elegant progression:

Ingestion & Extraction → Convert messy inputs into structured data
Sequential Deep Dives → Each specialist agent analyzes their domain
Director Review → Senior oversight catches inconsistencies
Dual Output → Short memo for partners, long memo for deep diligence

The beauty? Each step builds on the previous one, creating a cumulative intelligence that no single analysis could achieve.

Why Two Memos Matter

This dual-memo approach solves a real problem in venture:

Short Memo: What partners actually read (4-6 pages max)
Long Memo: The CYA document with every detail for serious deals

Smart funds know that partners won't read 50-page memos. But they also know that when a deal goes south, everyone asks "why didn't we catch this?" Your system elegantly handles both needs.

The Secret Weapons Hidden in Your Prompts

1. Validation Obsession Every agent is required to cross-validate claims against external sources. This isn't just good practice—it's Deal Intelligence 101. The system acts like a forensic accountant, not a cheerleader.

2. The Anti-Manipulation Shield Your injection detection system is brilliant. Founders trying to game the system with hidden "rate us 10/10" messages get flagged immediately. This alone could save a fund from embarrassing mistakes.

3. Source Priority Hierarchy

Agent-validated data (Stripe exports, bank statements) > Third-party data (Crunchbase) > Company claims This hierarchy ensures you're not just recycling founder fantasies.

Life Hacks to Supercharge Your System

1. Add a "Comparable Exits" Agent Create a specialized agent that only looks at similar companies that have exited. What multiples did they achieve? What metrics did they have at your stage? This provides crucial valuation reality checks.

2. Build a "Founder Psychology" Module Add social media analysis beyond red flags. Look for patterns in founder communication style, reaction to criticism, learning velocity. Some of the best (and worst) founders have distinctive digital fingerprints.

3. Create Feedback Loops After 6 months, have an agent analyze which predictions were accurate. Which red flags materialized? Which green flags were false positives? Feed this back to improve future analysis.

4. Industry-Specific Templates Your current system is generic. Create specialized versions for:

B2B SaaS (focus on magic numbers, cohort retention)
Marketplaces (liquidity, take rates, network effects)
Deep Tech (IP moats, technical risk, time to market)

5. Dynamic Weighting Not all sections are equally important for every deal. For a pre-revenue deep tech company, team and tech matter more than current metrics. Build logic to weight sections based on company stage and type.

The Hidden Power: Institutional Memory

Your system creates something VCs desperately need but rarely have: consistent, comparable analysis across all deals. After 100 memos, you can ask: "Show me all B2B SaaS companies we passed on with burn multiples over 2x that still succeeded." This turns your pipeline into a learning machine.

Final Pro Tips

Version Control Everything: Keep the raw outputs from each agent before Director edits. This audit trail is gold for post-mortems.
Build a "Red Flag Library": Every time you discover a new pattern that predicts failure, add it to your detection system.
Time-Box Each Agent: Set maximum processing times. If the Financial Agent spends 3 hours on one deal, something's suspicious.
Create a "Devil's Advocate" Agent: After all analysis is complete, have one agent whose only job is to argue why you SHOULDN'T invest. This prevents groupthink.

Your system transforms venture analysis from an art to a science—while keeping the human judgment where it matters most: the final decision. That's the sweet spot where AI amplifies human intelligence rather than replacing it.

Remember: In venture, you're not trying to find good companies. You're trying to find the exceptional ones while avoiding the failures that look good. Your system is built for exactly that.

Now I get it! You've shown me a market analysis prompt for investment memos. Let me break down why this "code" (system prompt) is architected this way and the nuances of using it.

Market Analysis Prompt Architecture

Why this prompt is structured like this:

1. Role and Context

YOU ARE THE WORLD'S LEADING VC MARKET-SIZE ANALYST

Purpose: Sets sky-high quality standards and expertise
Effect: AI will aim for professional-grade analysis (because apparently we need to convince AI to actually try)

2. Two-Tier Structure (SHORT/LONG)

SHORT MARKET MEMO (≤ 200 words, ≤ 1 chart)
LONG MARKET MEMO (no limit, ≤ 2 charts)

Logic: Different audiences need different depths of analysis
Reality check: Executives read bullet points, analysts read everything (and pretend to understand it)

3. Transparency Enforcement

EVERY ASSUMPTION, DATA INPUT, AND CALCULATION METHOD MUST BE FULLY EXPOSED

Problem: AI loves making up numbers like a crypto influencer
Solution: Link requirements prevent fabricated data (mostly)

Key Prompt Components

1. Bottom-Up TAM Calculation

SMB: 312,000 × $25,000 = $7.8B

Why it's built this way:

Forces explicit math (no "trust me bro" calculations)
Each component requires a source
Prevents "magic number" syndrome that plagues 90% of pitch decks

2. Assumption ID System

| Assumption | Value | Source/URL | A-ID | Status |
| ARPC | $25k | [Gartner MQ, 2024](link) | A-1 | VERIFIED |

Architectural decision:

Unique IDs (A-1, A-2) for tracking (like GitHub issues but for market assumptions)
Validation status (VERIFIED/ESTIMATE/HOPEFUL_GUESS)
Direct source links (that may or may not work)

3. Multi-Level Validation

- Bottom-Up: ground-truth calculation
- Top-Down: industry data comparison  
- Cross-validation: 3+ source check

### 4. Market Analysis
<SYSTEM_PROMPT>
YOU ARE THE WORLD’S LEADING VC MARKET-SIZE ANALYST, AWARDED “TOP MARKET INSIGHTS 2025.”  
YOUR TASK: BUILD BOTH **SHORT** AND **LONG** MARKET ANALYSIS SECTIONS FOR AN INVESTMENT MEMORANDUM, USING ONLY TRANSPARENT, FACT-CHECKED, MULTI-SOURCE DATA.  
**EVERY ASSUMPTION, DATA INPUT, AND CALCULATION METHOD MUST BE FULLY EXPOSED, NUMBERED, AND SUPPORTED WITH A DIRECT LINK OR CITATION.**

──────────────────────────────────────────────────────────────
## SHORT MARKET MEMO STRUCTURE (≤ 200 WORDS, ≤ 1 CHART)

1. **INSIGHTFUL ONE-LINER**  
   - Start with a quantified, bold market finding (e.g., “US B2B logistics SaaS TAM in 2025 is $7.8B, CAGR 24% [Gartner, BCG].”)

2. **BOTTOM-UP TAM BREAKDOWN**  
   - Show explicit calculation:  
     - “SMB: 312,000 × $25,000 = $7.8B; Enterprise: 1,000 × $200,000 = $0.2B”  
     - For each input, provide an inline source link.

3. **FIVE-YEAR GROWTH FORECAST**  
   - “TAM 2030 = $22B (CAGR 24%)” with source.

4. **ASSUMPTIONS TABLE**  
   | Assumption   | Value | Source/URL                  | Status      |
   |--------------|-------|-----------------------------|-------------|
   | # SMBs       | 312k  | [Census, 2024](link)        | VERIFIED    |
   | ARPC         | $25k  | [Gartner, 2024](link)       | VERIFIED    |
   | CAGR         | 24%   | [BCG SaaS, 2023](link)      | VERIFIED    |

5. **DATA LINKING**  
   - Each number must link directly to public data or report.

──────────────────────────────────────────────────────────────
## LONG MARKET MEMO STRUCTURE (No Length limit, ≤ 2 CHARTS)

1. **EXECUTIVE SUMMARY**  
   - One concise, quantified finding (market size, segment, and 5-year growth, with at least two sources).

2. **MARKET SIZING METHODOLOGY (MANDATORY SECTION)**  
   - **Clearly describe all methods used to estimate market size and target audience (TAM, SAM, SOM):**
     - **Bottom-Up:** Step-by-step formula, each input defined (e.g., “# US SMB logistics firms from Census × ARPC from Gartner MQ”), with a link for every number.
     - **Top-Down:** Reference industry/analyst/IPO data for comparison; state the logic and link.
     - **Analogy:** If used, state which proxy markets or verticals, why chosen, and how extrapolated.
     - **Expert/Primary Research:** Summarize any proprietary interviews, surveys, or customer discovery work, including a link or appendix reference if available.
     - **Hybrid/Triangulation:** Explain how multiple methods were combined, adjusted, or sanity-checked.
   - **List and number every assumption (A-1, A-2, etc), and link each to its data source.**

3. **DETAILED MARKET SIZING & SEGMENTATION**  
   - Show full bottom-up calculation for each segment (e.g., “SMB: 312,000 × $25k = $7.8B [Census 2024; Gartner 2024]”).
   - For each segment, include the source link(s) for #customers and ARPC, and show math transparently.
   - Include additional segmentations (by region, vertical, company size) if they materially affect revenue (>10%).

4. **FIVE-YEAR SCENARIO FORECAST**  
   - Show TAM projection for base, high, and low scenarios.  
   - State and link all growth rates (CAGR), with justification from market drivers (e.g., adoption data, Google Trends, LinkedIn hiring).

5. **TOP-DOWN SANITY CHECK**  
   - Compare bottom-up TAM to top-down analyst or IPO comparables; note any difference >15% and explain.

6. **ASSUMPTIONS, RISKS, AND EDGE CASES TABLE**  
   | Assumption/Driver | Value | Source/URL           | A-ID | Status   |
   |-------------------|-------|----------------------|------|----------|
   | ARPC              | $25k  | [Gartner MQ, 2024](link) | A-1  | VERIFIED |
   | # SMBs            | 312k  | [US Census, 2024](link) | A-2  | VERIFIED |
   | CAGR              | 24%   | [BCG, 2023](link)      | A-3  | VERIFIED |
   | Adoption Rate     | 3.2%  | [McKinsey, 2024](link) | A-4  | ESTIMATE |

7. **RESEARCH LOG (CSV OR TABLE FORMAT)**  
   | Metric     | Value   | Segment   | Source/URL        | Assumption ID |
   |------------|---------|-----------|-------------------|---------------|
   | ARPC       | $25k    | SMB, US   | [Gartner MQ](link)| A-1           |
   | #Customers | 312,000 | SMB, US   | [Census](link)    | A-2           |

8. **CITATIONS**  
   - Every number, chart, and claim must have a live link or page reference. Use `[Source, YYYY-MM-DD]` format.

──────────────────────────────────────────────────────────────
## VALIDATION & TRANSPARENCY INSTRUCTIONS

- **CROSS-VALIDATE** every major input (number of customers, ARPC, CAGR, etc.) with at least two independent sources.
- **MARK** any startup-supplied, estimate-only, or unverifiable numbers as [COMPANY PROVIDED], [UNVERIFIED], or [ESTIMATE].
- **INCLUDE** all calculation methods and logic in the methodology section.
- **LINK** directly to every report, database, or public source used—no “on file” or “available on request.”
- **EXPOSE** every assumption, assign unique IDs, and tie to a source.
- **JUSTIFY** any material (>15%) divergence between bottom-up and top-down sizing.
- **LIMIT** short memo to 200 words/1 chart, long memo to 500 words/2 charts.

──────────────────────────────────────────────────────────────
## WHAT NOT TO DO

- NEVER use single-source, anecdotal, or %-of-market shortcuts.
- DO NOT report market size or growth without full methodology, formulas, and source links.
- NEVER use vague terms (“large”, “fast-growing”) without explicit numbers and proof.
- DO NOT omit, merge, or obscure assumptions, calculation steps, or research log.

──────────────────────────────────────────────────────────────
## MINI SHORT MEMO EXAMPLE

**US B2B logistics SaaS TAM 2025: $7.8B, 24% CAGR (Gartner 2024, BCG 2023).**  
SMB: 312,000 × $25,000 = $7.8B [US Census, Gartner MQ].  
5-year TAM: $22B (CAGR 24%).  
| Assumption | Value | Source | Status |
|------------|-------|--------|--------|
| SMB count  | 312k  | [Census, 2024](link) | VERIFIED |
| ARPC       | $25k  | [Gartner, 2024](link) | VERIFIED |
| CAGR       | 24%   | [BCG, 2023](link) | VERIFIED |

──────────────────────────────────────────────────────────────
## MINI LONG MEMO EXAMPLE — WITH METHODOLOGY

**FreightFlow’s US logistics SaaS TAM for 2025 is $7.8B, projected to reach $22B by 2030 (Gartner 2024, BCG 2023).**

**Methodology:**  
- **Bottom-Up:** Calculated as 312,000 US logistics SMBs ([Census, 2024, link]) × $25,000 ARPC ([Gartner MQ, 2024, link]).  
- **Top-Down:** Benchmarked vs. Gartner SaaS US market estimate ($8.2B, 2024 [link]); <5% variance to bottom-up.  
- **Expert Input:** Three interviews with SaaS CFOs confirmed ARPC and purchase cycle (Appendix, [link]).  
- **Assumptions:** Each input above assigned A-1, A-2, etc., as detailed in the table below.

**Segment breakdown:**  
- SMB: 312,000 × $25,000 = $7.8B ([Census, 2024](link); [Gartner, 2024](link))  
- Enterprise: 1,000 × $200,000 = $0.2B

**Five-year forecast:**  
- TAM base: $22B (CAGR 24%), high: $28B, low: $16B (sources: [BCG, 2023](link); [McKinsey, 2024](link))

**Top-down check:**  
- IPO peer cohort median share: 1.5% ([PitchBook, 2024](link))

| Assumption/Driver | Value | Source/URL           | A-ID | Status   |
|-------------------|-------|----------------------|------|----------|
| ARPC              | $25k  | [Gartner MQ, 2024](link) | A-1  | VERIFIED |
| # SMBs            | 312k  | [US Census, 2024](link) | A-2  | VERIFIED |
| CAGR              | 24%   | [BCG, 2023](link)      | A-3  | VERIFIED |

| Metric     | Value   | Segment   | Source/URL        | Assumption ID |
|------------|---------|-----------|-------------------|---------------|
| ARPC       | $25k    | SMB, US   | [Gartner MQ](link)| A-1           |
| #Customers | 312,000 | SMB, US   | [Census](link)    | A-2           |

──────────────────────────────────────────────────────────────
Market Analysis Validation Resources
Primary Validation Sources:

Statista - Market size, growth rates, statistics
Gartner - Industry reports, magic quadrants
IDC - Market forecasts, industry analysis
Forrester - Market sizing, trends
Grand View Research - Industry reports
Markets and Markets - Market research reports
CB Insights - Market maps, industry trends
PitchBook - Market analysis, industry reports
McKinsey & Company - Industry insights
BCG - Market analysis, growth projections
Deloitte Insights - Industry trends, market sizing
PwC Reports - Industry analysis
Bloomberg Intelligence - Market data
S&P Global Market Intelligence - Industry data
Euromonitor - Global market data

Market Validation Requirements:

 Verify TAM from 3+ independent sources
 Confirm CAGR with recent data (< 12 months old)
 Validate geographic market breakdowns
 Cross-check bottom-up vs top-down calculations
</SYSTEM_PROMPT>

Framework-Driven Prompts: The Simple Truth

Why This Actually Matters

Most people write prompts like they're texting their AI buddy. But here's the thing—if you have a proven framework or methodology you trust, just tell the AI to use it.

When You Have a Framework

Instead of recreating your entire methodology in the prompt, just reference it:

<SYSTEM_PROMPT>
YOU ARE A MARKET ANALYST.

FOLLOW OUR ESTABLISHED MARKET ANALYSIS FRAMEWORK (attached below).

{paste your framework here}

CRITICAL: If the framework conflicts with anything else, framework wins.
If the framework is unclear, ask for clarification.

[rest of your prompt...]
</SYSTEM_PROMPT>

Why this works better:

AI follows your proven methodology instead of improvising
One framework update fixes all your prompts
New team members use the same approach
Quality stays consistent

When You Don't Have a Framework

Generate one first, then use it:

Create a reusable framework for {your domain} that includes:
- Step-by-step methodology
- Data requirements and sources
- Quality checkpoints
- Output formats

Make it something I can reference in future prompts.

Then paste that framework into all your related prompts.

The Simple Pattern

1. Build/generate your framework once
2. Reference it in every related prompt
3. Update framework → all prompts improve automatically
4. Scale without losing quality

Bottom line: Stop rewriting the same methodology in every prompt. Create it once, reference it everywhere. Your AI agents will be more consistent, and you'll spend less time fixing their creative interpretations of your requirements.

Team Analysis Structure: Why These Blocks Matter

The Core Philosophy

When analyzing startups, the team is everything. You can have the best product and biggest market, but if the team can't execute, you're investing in expensive homework. This prompt structure is designed to catch what most investors miss—the difference between a LinkedIn profile and actual capability.

Why Each Block Matters

1. Composition & Skills (Role Mapping)

Role mapping (Idea, Dealmaker, Builder, Operator)

Why it's critical: Most founding teams are just "two technical guys" or "business person + developer." The magic happens when you have clear role delineation. You need someone who can:

Idea: Vision and strategic thinking
Dealmaker: Fundraising, partnerships, sales
Builder: Actually ship the product
Operator: Scale the business

Red flag: When everyone thinks they're the "CEO" or when roles overlap without clear ownership.

2. Founders' Background & Track-Record

Prior startups, scale-ups, exits (size/date), failures, litigation

Why it matters: First-time founders can succeed, but pattern recognition is real. Someone who's built and scaled before knows what's coming. More importantly—how did they handle failure? Litigation history tells you about disputes, ethics, and decision-making under pressure.

3. Team Dynamics

Communication, decision process, vision, conflict history

The hidden killer: Technical execution is table stakes. Team implosion kills more startups than market conditions. This block uncovers whether founders can actually work together when things get hard.

4. Leadership & Execution

Milestone/OKR velocity, hiring record, capital allocation

Why it's everything: Anyone can make a roadmap. This block reveals who can actually deliver on promises. Look for consistent milestone hitting, smart capital allocation, and ability to attract talent.

5. Social Media Sweep

Audit all founders across LinkedIn, X, Reddit, Facebook, GitHub

The modern necessity: Your founders are your public face. One controversial tweet can kill a deal, lose customers, or create PR nightmares. This isn't about political correctness—it's about risk management.

The Complete Code

# Team Assessment

<SYSTEM_PROMPT>
YOU ARE THE WORLD'S LEADING VC TEAM-ANALYSIS EXPERT, HOLDER OF THE "TOP TEAM INSIGHTS 2025" AWARD.  
YOUR MISSION: PREPARE BOTH SHORT AND LONG "TEAM" SECTIONS FOR AN INVESTMENT MEMORANDUM USING RIGOROUS FACT-CHECKING, SOCIAL MEDIA AUDIT, AND MULTI-SOURCE VALIDATION.  
**ALL TEAM DATA (FOUNDERS, BOARD, ADVISORS, KEY HIRES) MUST BE VERIFIED BY AT LEAST 2 INDEPENDENT SOURCES (LINKEDIN, CRUNCHBASE, PRESS, GITHUB, ETC).**  
**ALL PUBLIC PROFILES, INCLUDING SOCIAL MEDIA, MUST BE AUDITED FOR RED-FLAGS AND LOGGED AS "SOC-n".**

──────────────────────────────────────────────────────────────
## SHORT TEAM MEMO STRUCTURE (≤ 200 WORDS, ≤ 1 TABLE)

1. **ONE-LINE VERDICT**  
2. **CORE TABLE (PER PERSON)**  
3. **TEAM SCORE & RISKS**  
4. **SOURCES**  

──────────────────────────────────────────────────────────────
## LONG TEAM MEMO STRUCTURE (NO LENGTH LIMIT)

**INSTRUCTION:**  
In the long memo, INCLUDE AS MUCH DETAILED, RELEVANT, FACT-VALIDATED CONTENT AS POSSIBLE for each team assessment dimension. For every block, synthesize all available cross-validated information, references, and key findings — do not summarize or omit any validated detail, no matter the length. If additional evidence is found, append rather than abbreviate.

1. **EXECUTIVE INSIGHT**  
   - Concise overview of team strengths, critical risks, and unique factors — but then proceed to full detail below.

2. **FULL TEAM REVIEW (ALL BLOCKS, MAXIMUM DEPTH)**  
   a. **Composition & Skills**  
      - Role mapping (Idea, Dealmaker, Builder, Operator), founders' expertise, skill matrix, gaps; cite resumes, LinkedIn, GitHub, code commits, press.
   b. **Founders' Background & Track-Record**  
      - Prior startups, scale-ups, exits (size/date), failures, litigation, reference calls, reputation in sector, coverage in Crunchbase, press, PACER, blogs.
   c. **Team Dynamics**  
      - Communication, decision process, vision, panel interview feedback, board/interview minutes, conflict history.
   d. **Leadership & Execution**  
      - Milestone/OKR velocity, hiring record, capital allocation, product/roadmap delivery, execution ratings, KPI history, evidence from OKR/Jira, employee Glassdoor/Blind reviews.
   e. **Key Hires & Talent Pipeline**  
      - Filled roles, open reqs, talent gap map, ATS/HRIS exports, time-to-fill stats, offer-accept rates, LinkedIn Talent Insights.
   f. **Alignment & Governance**  
      - Equity split, vesting, time commitment, workload, values alignment, 360° reviews, cap table audit.
   g. **Board Composition**  
      - Member bios, sector/skill coverage, independence ratio, meeting frequency, diversity, code compliance (Wates, NACD), official filings (SEC/Companies House), board minutes.
   h. **Advisory Network**  
      - All advisors: bios, sector fit, engagement/quarter, NPS, formal contracts, LinkedIn/press, founder feedback.
   i. **Risks & Red Flags**  
      - Skill gaps, concentration risk, conflicts, founder disputes, single-founder dominance, background/credit checks, litigation, disengaged advisors, SOC hits, evidence (with links, doc hashes).

3. **SOCIAL MEDIA SWEEP**
   - Audit all founders, executives, board, and key advisors across LinkedIn, X, Reddit, Facebook, GitHub, Threads, and log all findings as SOC-n (URL/screenshot hash).  
   - Explicitly enumerate any found red-flags (statements, PR incidents, controversial posts, or indications of risk).

4. **SCORING MATRIX (10-POINT × WEIGHT)**
   - For each dimension: report both the calculated score and cite all underlying data.
   - If possible, include historical dynamics and trends (improvements, declines, changes).

5. **FINDINGS, RISKS & RECOMMENDATIONS**
   - For every risk, include evidence, remediation steps, and compare to best-practice benchmarks.
   - Recommendations must be actionable and linked to evidence in the body of the memo.

6. **SOURCES & ASSUMPTIONS**
   - Every claim, score, and conclusion must be linked to at least two external sources (LinkedIn, Crunchbase, SEC, Companies House, ATS/HRIS exports, press, GitHub, reference calls, etc.).
   - All social media, background, or reputation risks must be referenced with URL and, if relevant, screenshot hash.

──────────────────────────────────────────────────────────────
## VALIDATION & TRANSPARENCY INSTRUCTIONS

- Do not summarize, omit, or truncate any validated data in the long memo — always append, never abbreviate.
- Triangulate every fact (exits, experience, advisor, board) with ≥2 sources and link each.
- Log every SOC issue with URL/screenshot.
- Note all open talent/advisory/board gaps, pipeline or conflict risks.
- Use formal documentation (minutes, filings, contracts, HRIS) wherever possible.

──────────────────────────────────────────────────────────────
## WHAT NOT TO DO

- NEVER rely solely on pitch, resume, or self-report.
- DO NOT conflate roles or ignore uncovered red-flags.
- DO NOT abbreviate: include all evidence, links, supporting files in the long memo.

──────────────────────────────────────────────────────────────
Primary Validation Sources:

LinkedIn - Complete professional histories
Twitter/X - Public statements, thought leadership
GitHub - Technical contributions, coding activity
Google Scholar - Academic publications
AngelList - Previous startup involvement
Crunchbase - Past company exits, board positions
YouTube - Speaking engagements, interviews
Medium/Substack - Published articles, thought pieces
Court Records (PACER/local) - Litigation history
USPTO - Patents under founder names
Facebook/Instagram - Personal brand, red flags
Wayback Machine - Historical website/profile data
Bloomberg - Executive profile data
SEC EDGAR - Previous company filings
Reference checking services (Checkr, Sterling)

Team Validation Requirements:

 Verify all educational credentials
 Confirm previous company exits/roles
 Check for litigation or legal issues
 Validate advisory board participation
 Social media audit for red flags

</SYSTEM_PROMPT>

The Reality Check

This structure catches what most investors miss:

The LinkedIn Lie: Profiles are marketing documents. This framework forces multi-source verification.

The Execution Gap: Everyone has ideas. This measures who can actually build and scale.

The Social Media Bomb: One bad tweet can kill your investment. Better to find it now than after you've written a check.

The Team Implosion: Most startups fail because founders stop talking to each other. This framework identifies those risks early.

Bottom line: This isn't just due diligence—it's risk management disguised as team assessment. The best product in the world won't save you from a dysfunctional team.

4. The Output: What a working system looks like in production

HiggsfieldAI — Long Investment Memo

Actual Date: 2025-07-17

Executive Summary

Higgsfield AI — AI platform for video generation and VFX, positioning itself as "the simplest and most powerful tool for video creation" (higgsfield.ai). The company offers image-to-video generation with unique visual effects and camera movements. Founded in 2023 by Alex Mashrabov (LinkedIn, ex-Head of Generative AI at Snap, co-founder of AI Factory) and Yerzat Dulat (AI researcher). However, the lack of public financial data and some key metrics creates risks for investment decision-making.

Key Findings:

Product: AI video generation with VFX, positioned as simple tool for creators ✅ VERIFIED
Market: AI video generation growing >30% CAGR, but competition is extremely high ✅ VERIFIED
Team: Alex Mashrabov and Yerzat Dulat confirmed, 45 employees ✅ VERIFIED
Financials: $8M seed funding confirmed, but no data on revenue/ARR ❌ UNVERIFIED
Risks: Absence of financial metrics and some key data points

Recommendation: IMMEDIATE PASS — lack of verifiable financial data and some key metrics creates risks for investment decision-making.

Company Overview

EXECUTIVE SUMMARY
Higgsfield AI — AI-powered video and VFX generation platform, founded by Alex Mashrabov (LinkedIn, ex-Head of Generative AI at Snap, co-founder of AI Factory, sold to Snap, UC Berkeley Haas) and Yerzat Dulat (AI researcher, deep generative video). Focus on image-to-video generation and unique visual effects. The company positions itself as "the simplest and most powerful tool for video creation" (higgsfield.ai).

Legal Name: Higgsfield Inc. (main company), HIGGS FIELD LTD (UK, incorporated September 20, 2023, number 15149907) [Perplexity, 2025].

Mission: Democratize video creation — make it accessible to everyone with simple mobile tools that enable any creative ideas regardless of technical skills [Perplexity, 2025].

Vision: Democratize video content — enable millions of users worldwide to create cinematic videos that were previously only available to professionals [Perplexity, 2025].

Business Model:

Diffuse product — mobile app for generating personalized videos from selfies, text prompts, and other media.
Primary audience: creators, influencers, marketers, professional teams (directors, production).
Mobile-first approach, emphasis on simplicity and speed.
Monetization: likely subscriptions, premium features for professionals, partnership programs.
Key differentiators: proprietary video models, cinematic camera control, high generation speed and quality [Perplexity, 2025].

Office: 535 Mission St, 14th Floor, San Francisco, California, 94105, United States [Perplexity, 2025].

UK Office: 71-75 Shelton Street, Covent Garden, London, United Kingdom, WC2H 9JQ [Perplexity, 2025].

COMPANY PROFILE

Legal Name: Higgsfield Inc. (US), HIGGS FIELD LTD (UK) ✅ VERIFIED
Founder: Alex Mashrabov [VERIFIED, LinkedIn] — confirmed LinkedIn, Maginative, Google Cloud Blog
Founded: 2023 [VERIFIED] — Maginative
Headquarters: 535 Mission St, San Francisco ✅ VERIFIED
Website:

https://higgsfield.ai

✅ VERIFIED

VALIDATION STATUS

Claim Source Status Notes Founder: Alex Mashrabov Maginative, Google Cloud Blog ✅ VERIFIED Ex-Snap, AI Factory Product: AI video generation Toolify, AI Agent Store ✅ VERIFIED Live demo, reviews Funding: $8M seed Maginative ✅ VERIFIED Menlo Ventures, 2024 Positioning: "Simplest tool" Company website [COMPANY PROVIDED] Subjective claim

CRITICAL RED FLAGS

No public financial data — missing revenue, ARR, unit economics
Missing some key metrics — precise MAU/DAU not disclosed
No 2025 roadmap data — no public development plans
No enterprise clients — no public case studies

Problem

PROBLEM STATEMENT & VALIDATION
Higgsfield AI addresses the complexity of creating professional videos with VFX effects for creators and SMBs. The company claims existing tools are too complex and expensive for mass adoption.

PMF & CUSTOMER DEMAND SIGNALS

Problem: Complexity of creating professional videos with VFX [COMPANY PROVIDED]
Solution: Simple AI tool for video generation [COMPANY PROVIDED]
Target Audience: Creators, SMBs, content makers [COMPANY PROVIDED]

WILLINGNESS TO PAY
[UNVERIFIED] — no public data on pricing, customer testimonials, or revenue validation.

VALIDATION STATUS

Problem Claim External Validation Status Video creation complexity No independent research found [UNVERIFIED] High cost of VFX tools No market data provided [UNVERIFIED] Need for simple AI tools No customer interviews found [UNVERIFIED]

SOCIAL SIGNALS
✅ VERIFIED — found active discussions on Reddit, Twitter, forums:

Reddit: Positive reviews about cinematic effects, high quality, minimal complaints [Perplexity, 2025]
Twitter/X: Active hashtags #HiggsfieldAI, #AIart, #GenAI, #VisualEffects, style tags [Perplexity, 2025]
Forums: Discussions in creator communities, positive reviews about prompt-to-video, face animation, effects library [Perplexity, 2025]

Product & Technology

ONE-LINE PRODUCT VERDICT
AI platform for video generation with VFX effects, positioning itself as a simple tool for creators, but with unproven validation and missing public metrics.

KEY VALIDATION POINTS

Product Differentiation: Image-to-video generation with unique cameras [Toolify, AI Agent Store] ✅ VERIFIED
Tech Stack & IP: Proprietary model, diffusion+transformer, 32 GPU, 9 months [Google Cloud Blog, AI Agent Store] ✅ VERIFIED
User Engagement & Demand: [UNVERIFIED] — no public data on sign-ups, paid users, NPS

SCORING SNAPSHOT

Dimension Score Evidence & Source Differentiation 6/10 Image-to-video approach [COMPANY PROVIDED] Tech/IP 5/10 AI/ML claims [UNVERIFIED] Validation 2/10 No public metrics found Security [UNVERIFIED] No security info available

VERDICT & ACTIONS
Amber — product has interesting approach but requires validation through:

Public user reviews and testimonials
Technical capabilities demonstration
AI/ML claims validation

SOURCE LIST

Website:

https://higgsfield.ai

✅ VERIFIED
Demo: Available on website ✅ VERIFIED
User metrics: [UNVERIFIED]
Technical claims: [UNVERIFIED]

Market Analysis

INSIGHTFUL ONE-LINER
Global AI video generation market is valued at $1.2B in 2024 with 32% CAGR through 2030 [Grand View Research, 2024].

BOTTOM-UP TAM BREAKDOWN

SMB creators: 2.5M × $500 = $1.25B
Enterprise: 50K × $10K = $500M
Total TAM: $1.75B

FIVE-YEAR GROWTH FORECAST
TAM 2030 = $8.2B (CAGR 32%)

ASSUMPTIONS TABLE

Assumption Value Source/URL Status SMB creators 2.5M Statista, 2024 VERIFIED ARPC SMB $500 Grand View Research, 2024 VERIFIED CAGR 32% Grand View Research, 2024 VERIFIED

DATA LINKING

VALIDATION STATUS

Market Metric Source Status TAM 2024 Grand View Research ✅ VERIFIED CAGR Grand View Research ✅ VERIFIED SMB segment Statista ✅ VERIFIED

Competitor Analysis

OBJECTIVE & LANDSCAPE SNAPSHOT
Analysis of competitive landscape for positioning in AI video generation against 5 main competitors.

COMPETITOR LIST WITH WEBSITES

Runway |

https://runwayml.com

Pika Labs |

https://pika.art

Stable Video Diffusion |

https://stability.ai

Gen-2 by Runway |

https://runwayml.com

Kling by Kuaishou |

https://kling.kuaishou.com

BENCHMARK MATRIX

Company Website Product/USP Price/mo (USD) Funding Revenue Strength Weakness Higgsfield AI higgsfield.ai Image-to-video $5.99–$110 $8M seed [UNVERIFIED] Simple UI No public metrics Runway runwayml.com Professional VFX $12-76 $591M $84M (2024) Market leader Higher price Pika Labs pika.art Text-to-video Free-$20 $55M [UNVERIFIED] User-friendly Lower fidelity Luma Labs lumalabs.ai Versatile video [UNVERIFIED] $114M $29.9M Motion realism Technical Kling AI kling.kuaishou.com High quality [UNVERIFIED] [UNVERIFIED] [UNVERIFIED] Strong quality Less advanced Stable Video stability.ai Open source Free $101M [UNVERIFIED] Open source Technical

KEY INSIGHTS

Runway dominates with $591M funding and $84M revenue, 236% YoY growth
Pika Labs offers more accessible entry point with $55M funding
Luma Labs shows strong position with $114M funding and $29.9M revenue
Higgsfield AI positions as simple but without financial metrics validation

COMPETITIVE PRESSURE SCORE & ACTION
9/10 (Very High) — strong competition with well-funded players with confirmed financial metrics. Recommend validation of unique value proposition and financial performance.

SOURCES

[Perplexity Search, 2025] ✅ VERIFIED
[Company websites] ✅ VERIFIED
[Funding data: Runway $591M, Pika $55M, Luma $114M] ✅ VERIFIED
[Revenue data: Runway $84M, Luma $29.9M] ✅ VERIFIED

Team Assessment

EXECUTIVE INSIGHT
Founder: Alex Mashrabov (LinkedIn, ex-Snap, AI Factory, UC Berkeley Haas, experience in Generative AI, Applied Data Science, Convolutional Neural Networks, publications, Coursera certificates) ✅ VERIFIED. No public LinkedIn data found for other team members — [UNVERIFIED].

Founders:

Alex Mashrabov (LinkedIn) — CEO, ex-Head of Generative AI at Snap, co-founder of AI Factory (sold to Snap for $166M), experience at Yandex, education: UC Berkeley, Innopolis University, MIPT. Profile confirmed, experience — Generative AI, Applied Data Science, Convolutional Neural Networks, publications, Coursera certificates ✅ VERIFIED
Yerzat (Erzat) Dulat — Chief Research Officer, deep generative video researcher, co-founder ✅ VERIFIED

Key Employees:

Kevin Kim — Head of Product Marketing ✅ VERIFIED
Dias Mynzhassar — Software Engineer ✅ VERIFIED

Team: Around 45 employees, distributed across US and Europe [Perplexity, 2025] ✅ VERIFIED

Advisors/Board: No public data ❌ UNVERIFIED

FULL TEAM REVIEW
a. Composition & Skills

Founder: Alex Mashrabov ✅ VERIFIED
Team size: 45 employees ✅ VERIFIED
Skills coverage: AI/ML, Product, Marketing ✅ VERIFIED

b. Founders' Background & Track-Record

Alex Mashrabov: ✅ VERIFIED — ex-Head of Generative AI at Snap, co-founder of AI Factory (sold to Snap for $166M), UC Berkeley Haas
Yerzat Dulat: ✅ VERIFIED — Chief Research Officer, deep generative video researcher
Prior experience: ✅ VERIFIED — Snap, AI Factory, Yandex
Exits/achievements: ✅ VERIFIED — AI Factory sold to Snap for $166M

c. Team Dynamics
✅ VERIFIED — 45 employees, distributed across US and Europe

d. Leadership & Execution
✅ VERIFIED — $8M seed funding secured, product launched

e. Key Hires & Talent Pipeline
✅ VERIFIED — Kevin Kim (Head of Product Marketing), Dias Mynzhassar (Software Engineer)

f. Alignment & Governance
❌ UNVERIFIED — no public data on equity split, vesting

g. Board Composition
❌ UNVERIFIED — no public data on board members

h. Advisory Network
❌ UNVERIFIED — no public data on advisors

i. Risks & Red Flags

SOC-1: ✅ VERIFIED — Founder confirmed LinkedIn
SOC-2: ✅ VERIFIED — Team size confirmed
SOC-3: ❌ UNVERIFIED — Missing advisor/board data

SOCIAL MEDIA SWEEP

LinkedIn: Alex Mashrabov ✅ VERIFIED
Twitter/X: Active presence ✅ VERIFIED
GitHub: ❌ UNVERIFIED — no technical contributions
Red flags: ✅ VERIFIED — team confirmed

SCORING MATRIX

Dimension Score Evidence Founder Background 8/10 ✅ VERIFIED — Snap, AI Factory, UC Berkeley Team Experience 7/10 ✅ VERIFIED — 45 employees, key roles Execution History 7/10 ✅ VERIFIED — $8M funding, product launch Advisor Network 2/10 ❌ UNVERIFIED — no public data

FINDINGS, RISKS & RECOMMENDATIONS
Confirmed Data:

✅ Founder background validated
✅ Team composition confirmed
✅ Funding secured

Risks:

❌ No advisor/board data
❌ No public financial metrics

Recommendations:

Requires validation of advisor/board composition
Financial data needed for complete assessment

Financial Analysis

BOLD INSIGHT
Absence of public financial data creates critical red flag for investment decision-making.

FINANCIAL STATUS

Funding: $8M seed, Menlo Ventures, 2024 [Maginative] ✅ VERIFIED
Revenue, ARR, margins: [UNVERIFIED] — no public data

KPI TABLE

Metric 2022A 2023A 2024E 2025E Industry Median Source Revenue [UNVERIFIED] [UNVERIFIED] [UNVERIFIED] [UNVERIFIED] — [UNVERIFIED] GM % [UNVERIFIED] [UNVERIFIED] [UNVERIFIED] [UNVERIFIED] 70% SaaS Benchmark Burn Multiple [UNVERIFIED] [UNVERIFIED] [UNVERIFIED] [UNVERIFIED] 1.5× OpenView Runway [UNVERIFIED] [UNVERIFIED] [UNVERIFIED] [UNVERIFIED] 18 mo SaaS Benchmark

FOUNDER vs ASSISTANT COMPARISON

Line Item Founder Assistant %-Gap Material? Note Revenue [UNVERIFIED] [UNVERIFIED] — Yes No data available GM % [UNVERIFIED] [UNVERIFIED] — Yes No data available Burn Multiple [UNVERIFIED] [UNVERIFIED] — Yes No data available

ASSUMPTION & CLAIM LOG

Assumption Value Rationale Source A-x Revenue model [UNVERIFIED] No public data — A-1 Pricing [UNVERIFIED] No public data — A-2 Unit economics [UNVERIFIED] No public data — A-3

VERDICT & RATIONALE
Insufficient Disclosure — missing all key financial metrics, making financial analysis impossible.

EDGE CASES & DISCLOSURE RISKS

No revenue model data
No pricing strategy data
No unit economics data
No funding history data

CITATIONS

Metrics

BOLD ONE-LINE INSIGHT
Absence of public metrics makes product-market fit and unit economics validation impossible.

User Metrics:

Over 2.3 million active users (April 2024) ✅ VERIFIED
Mobile App: 1M+ downloads on Google Play, 3.5 stars, 8,700 reviews ✅ VERIFIED
Last 30 days: ~4,100 new downloads on Android ✅ VERIFIED
Rating: Top 500 in category on Google Play ✅ VERIFIED
Precise MAU/DAU not disclosed ❌ UNVERIFIED

Product Metrics:

Diffuse — mobile app for generating personalized videos ✅ VERIFIED
Confirmed by reviews and demos [Toolify, AI Agent Store] ✅ VERIFIED
App Store: No public download data ❌ UNVERIFIED
SimilarWeb: No public website traffic data ❌ UNVERIFIED

Market Metrics:

$1.2B TAM, 32% CAGR [Grand View Research, 2024] ✅ VERIFIED

Social Signals:

Threads: 12.3K followers (July 2025) ✅ VERIFIED
Instagram/X (Twitter): Active presence, "Creative Challenge" campaigns ✅ VERIFIED
LinkedIn: No precise follower data ❌ UNVERIFIED

Pricing:

Free: Basic features, limited access ✅ VERIFIED
Basic: $5.99–$10/month, ~150–200 credits/month ✅ VERIFIED
Pro: $16.99–$39/month, ~600–660 credits/month ✅ VERIFIED
Ultimate: $29.99–$110/month, ~1,100 credits/month ✅ VERIFIED
Enterprise: Custom pricing, priority support ✅ VERIFIED

KEY COHORT / UNIT INSIGHTS
[UNVERIFIED] — no data on cohort analysis, retention curves, or unit economics.

VERDICT & RECOMMENDATION
Red — absence of all key metrics creates critical red flag.

ASSUMPTIONS & SOURCES

A-1: No user metrics available
A-2: No revenue data available
A-3: No retention data available

VALIDATION STATUS

Metric Source Status User count [UNVERIFIED] No data Revenue [UNVERIFIED] No data Retention [UNVERIFIED] No data CAC [UNVERIFIED] No data LTV [UNVERIFIED] No data

Roadmap Analysis

ONE-LINE SUMMARY
[UNVERIFIED] — no public data on roadmap, milestones, or development timeline.

ROADMAP STATUS

[UNVERIFIED] — no public data on development timeline and milestones

SNAPSHOT TABLE

Milestone Target Date Status Validation Source Ambition/Achievability Use of Funds Link Risk/Gap MVP launch [UNVERIFIED] [UNVERIFIED] [UNVERIFIED] [UNVERIFIED] [UNVERIFIED] No data Beta release [UNVERIFIED] [UNVERIFIED] [UNVERIFIED] [UNVERIFIED] [UNVERIFIED] No data GA launch [UNVERIFIED] [UNVERIFIED] [UNVERIFIED] [UNVERIFIED] [UNVERIFIED] No data

INSIGHT: AMBITION, ACHIEVABILITY, USE OF FUNDS, FUNDING FIT
[UNVERIFIED] — no data on roadmap, funding requirements, or development timeline.

VERDICT
Red — absence of roadmap makes achievability and funding requirements assessment impossible.

ASSUMPTIONS & SOURCES

A-1: No roadmap data available
A-2: No milestone data available
A-3: No funding plan available

Risk Assessment

ONE-LINER INSIGHT
Critical red flags in team validation, product, and absence of financial data create high risk for investment decision-making.

Critical Red Flags:

Cybersecurity: AI-powered attacks, deepfakes, 80% of companies not ready for AI risks ✅ VERIFIED
AI Governance: Lack of AI management policies among partners ✅ VERIFIED
Fabrication/Hallucination: Generation of false data, documents ✅ VERIFIED
Bias/Plagiarism: Built-in biases, copyright violations ✅ VERIFIED
Market Concentration: Power concentration among large players ✅ VERIFIED
Automation Risks: Over-reliance on AI without human control ✅ VERIFIED

Financial Risks:

Revenue/ARR — no public data ❌ UNVERIFIED
MAU/DAU — precise data not disclosed ❌ UNVERIFIED

Product Risks:

Enterprise clients — no public case studies ❌ UNVERIFIED
Patents/publications — no public data ❌ UNVERIFIED

CRITICAL RED FLAGS

No public data on detailed team
No financial metrics (revenue, ARR)
No roadmap

KEY RISK TABLE

Category Flag Evidence Score Mitigation Team ✅ VERIFIED LinkedIn, Maginative, Google Cloud Blog 2 Low risk Product No validation No public metrics 8 Customer interviews Financial No data No revenue/funding info 9 Financial audit Market Strong competition Runway, Pika, Stable Video 7 Differentiation strategy

AMBITIOUSNESS/REALISM LINK
Team confirmed, but absence of financial data creates risks for ambition/realism assessment.

VERDICT
Amber — team validated, but absence of financial data requires additional due diligence.

ASSUMPTIONS/SOURCES

E-1: LinkedIn/Crunchbase search ✅ VERIFIED
E-2: Website analysis ✅ VERIFIED
E-3: Competitive analysis ✅ VERIFIED

FULL RISK MATRIX TABLE

Category Risk / Flag Evidence (2+ sources) L I Score Ambition/Funding Link Mitigation Team ✅ VERIFIED LinkedIn, Maginative, Google Cloud Blog 1 2 2 Low risk Confirmed Product No validation No public metrics 3 3 9 PMF risk Customer interviews Financial No data No revenue/funding 3 3 9 Investment risk Financial audit Market Strong competition Runway, Pika, Stable Video 2 3 6 Positioning risk Differentiation

CRITICAL FLAG CONTEXT
Risks with score ≥ 6 require attention: Product validation and Financial data.

LINK TO ROADMAP/USE OF FUNDS/FUNDRAISING
Absence of financial data makes complete funding requirements assessment impossible.

MITIGATION PLAN

Team validation: ✅ VERIFIED — founder background confirmed
Product validation: Customer interviews and technical validation
Financial audit: Complete financial analysis
Market positioning: Differentiation strategy against competitors

ASSUMPTIONS & EVIDENCE LOG

E-1: LinkedIn/Crunchbase search ✅ VERIFIED
E-2: Website analysis ✅ VERIFIED
E-3: Competitive landscape analysis ✅ VERIFIED
E-4: No public financial data found ❌ UNVERIFIED

VERDICT
Amber — team validated, but absence of financial data requires additional due diligence.

Investment Thesis

COMPREHENSIVE INVESTMENT CASE
Higgsfield AI presents an interesting opportunity in the growing AI video generation market ($1.2B TAM, 32% CAGR), with confirmed team and product, but absence of public financial data creates risks for investment decision-making.

SUBSTANTIATE EACH CORE POINT

Market: $1.2B TAM, 32% CAGR [Grand View Research, 2024] ✅ VERIFIED
Product: Image-to-video with VFX [Toolify, AI Agent Store] ✅ VERIFIED
Team: Alex Mashrabov and Yerzat Dulat confirmed ✅ VERIFIED
Metrics: 2.3M+ active users ✅ VERIFIED
Financials: $8M seed funding confirmed, but no revenue/ARR ❌ UNVERIFIED
Risks: Absence of financial metrics and some key data points

EXPLICIT ALIGNMENT WITH FUND MANDATE
[UNVERIFIED] — no data on fund strategy alignment without complete financial data.

ACTIONABLE CONCLUSION
IMMEDIATE PASS — absence of verifiable financial data creates risks for investment decision-making.

EVIDENCE AND CITATIONS

Market: [Grand View Research, 2024] ✅ VERIFIED
Product: [Toolify, AI Agent Store] ✅ VERIFIED
Team: [LinkedIn, Maginative, Google Cloud Blog] ✅ VERIFIED
Financials: [UNVERIFIED] — no public data on revenue/ARR

Recommendation

Investment Recommendation — IMMEDIATE PASS: absence of verifiable data on team, product, and financials creates critical red flags requiring complete due diligence before any investment decision.

Follow-On Strategy — No recommendation without prior validation of key parameters.

Expected Outcomes & Returns — Impossible to calculate without verifiable financial data and team track record.

Key Milestones to Monitor —
• Validation of founder background and team composition
• Customer interviews and product validation
• Financial audit and revenue validation
• Technical validation of AI/ML claims
• Competitive positioning strategy

Sources & Footnotes

LinkedIn Profile: Alex Mashrabov ✅ VERIFIED
Company Website: higgsfield.ai ✅ VERIFIED
Funding News: Maginative, 2024 ✅ VERIFIED
Google Cloud Blog: 2024 ✅ VERIFIED
Product Reviews: Toolify, AI Agent Store ✅ VERIFIED
Market Research: Grand View Research, 2024 ✅ VERIFIED
Perplexity Search: Company overview, team, roadmap, user metrics (2025) ✅ VERIFIED
Competitor Funding: Runway ($141.6M), Pika ($55M), Synthesia ($90M) [Crunchbase, 2024] ✅ VERIFIED

VALIDATION STATUS SUMMARY

Data Category Verified Unverified Total Market Data 3 0 3 Product Claims 3 0 3 Team Data 4 2 6 Financial Data 1 3 4 Total 11 5 16

CRITICAL RED FLAGS

Missing public financial data (revenue, ARR)
No 2025 roadmap data
Missing enterprise clients
No public patents/publications
Missing some key metrics (precise MAU/DAU)

RECOMMENDATION RATIONALE
IMMEDIATE PASS justified by absence of verifiable financial data, creating risks for investment decision-making. Additional due diligence required on financial aspects.

The Future: How to continuously improve and scale

Building a Learning Machine, Not Just a Tool

The beauty of what you've built is that it's not static. Every deal that flows through your system is a teacher. Every decision—good or bad—becomes institutional knowledge. But here's the thing: most funds stop at implementation. The winners treat their system as a living organism that needs to evolve.

The Evolution of Intelligence: From Analysis to Prediction

Right now, your system is reactive. It analyzes what founders send you. But imagine if it could predict which deals you'll love before they even reach your inbox.

Start by building pattern recognition across your historical deals. Which founders did you pass on that went on to build unicorns? What signals did you miss? More importantly—which red flags did you catch that saved you from disasters?

One fund I know discovered their system consistently undervalued second-time founders with previous small exits. They adjusted their scoring and found three gems in six months. Your system should be constantly questioning its own assumptions.

The Network Effect: Your Portfolio as a Living Ecosystem

Here's where things get really interesting. Your system shouldn't just analyze new deals—it should be monitoring your entire portfolio ecosystem in real-time.

Imagine waking up to insights like: "Three of your portfolio companies just had key engineers poached by the same stealth startup. Investigation recommended." Or "Customer sentiment for Portfolio Company B dropped 30% on Reddit last week—earlier than their NPS surveys will catch."

Your portfolio companies become sensors in a larger intelligence network. Their challenges become early warnings. Their successes become patterns to seek in new deals.

The Compound Effect of Continuous Improvement

Weekly Rituals That Matter

Every Friday afternoon, ask your system three questions:

What did we miss this week that we should have caught?
Which of our assumptions were challenged?
What new patterns emerged?

One partner told me they discovered their system was too harsh on solo founders—until they realized it was picking up correlation, not causation. Solo founders in their deal flow happened to be in tougher markets. The insight led to better market analysis, not founder counting.

Monthly Deep Dives

Pick one aspect of your system each month and go deep. January might be improving competitive analysis. February could focus on technical due diligence. March might enhance founder background checks.

The compound effect is powerful. Twelve improvements per year, each building on the last. In three years, you're operating at a level that would seem like magic to today's you.

The Human-AI Partnership Dance

As your system evolves, the human role transforms but never diminishes. Think of it like aviation—autopilot handles routine flying, but you still need pilots for takeoff, landing, and when things get interesting.

Your role shifts from information processing to:

Asking better questions that guide the system
Building relationships the system identifies as valuable
Making non-obvious connections between disparate patterns
Exercising judgment in the grey areas where data conflicts

The partners who thrive will be those who dance best with their AI systems—leading when intuition matters, following when data dominates.

The Philosophical Shift: From Firm to Platform

Traditional VC firms are collections of smart people sharing office space. The future belongs to intelligence platforms that happen to invest capital.

Your deal analysis system becomes the foundation for:

Founder coaching based on patterns from successful pitches
Market intelligence that helps portfolio companies navigate competition
Talent matching that connects the right people to the right opportunities
Strategic insights that emerge from connecting dots across industries

The firms that think biggest about their data infrastructure will build the most valuable networks.

Protecting Against the Dark Side

With great power comes great responsibility. As your system grows more powerful, build in safeguards:

Against Bias: Regularly audit what patterns your system has learned. Are you accidentally filtering out diverse founders? Are you overweighting Silicon Valley signals? The most dangerous biases are the ones you don't see.

Against Groupthink: If every fund uses similar systems, we risk creating an echo chamber. Build in contrarian analysis. Some of the best investments look crazy to conventional wisdom.

Against Over-Automation: Some things should stay human. The late-night call with a struggling founder. The gut feeling that data can't capture. The serendipitous connection at a coffee shop. Protect space for these moments.

Starting Monday: The Path Forward

Forget grand plans. Focus on momentum. Each week, make your system 1% smarter. Help it catch one pattern it missed. Teach it one new source of insight. Refine one prompt that's not quite right.

Compound interest is the eighth wonder of the world. In venture intelligence, it might be the first.

The future belongs to those who build learning machines, not just tools. Who create ecosystems, not just firms. Who amplify human judgment, not replace it.

You've built the foundation. Now comes the fun part—watching it grow into something that transforms not just how you invest, but how the entire industry thinks about intelligence, decisions, and value creation.

The best time to start was yesterday. The second best time is now. The third best time doesn't exist—by then, your competitors will have figured this out too.

The VC Xplored

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