FA Advisor is an AI-powered financial advisory system designed to help startups raise funding and investors evaluate opportunities. It provides comprehensive analysis, professional documentation, and intelligent investor matching - all powered by advanced algorithms and data-driven insights.

Core Capabilities

Project Assessment - 5-dimensional evaluation of investment readiness
Pitch Deck Generation - Professional 12-slide pitch deck outlines
Business Plan Creation - Comprehensive business plans with all essential sections
Valuation Analysis - Multi-method startup valuation (Scorecard, Berkus, Risk Factor, Comparables)
Investor Matching - Intelligent matching with investor database
Investment Analysis - Professional investment memos and DD checklists for investors
PDF Processing - Advanced PDF parsing, OCR, and report generation (Python advantage)

Quick Start

Installation

# Install dependencies
pip install -e .

# Install system dependencies for PDF processing (optional but recommended)
# macOS
brew install tesseract ghostscript poppler

# Ubuntu/Debian
sudo apt-get install tesseract-ocr poppler-utils ghostscript

Basic Usage

import asyncio
from fa_advisor import FAAdvisor
from fa_advisor.types import Project, Product, Market, Team, Financials, Fundraising

async def main():
    # Initialize advisor
    advisor = FAAdvisor()

    # Define your project
    project = Project(
        name="CloudFlow AI",
        description="AI workflow automation for enterprises",
        industry="enterprise-software",
        business_model="b2b-saas",
        product=Product(
            description="AI-powered workflow automation platform",
            stage="launched",
            key_features=["No-code builder", "AI integration", "Enterprise security"],
            unique_value_proposition="Reduce manual work by 80% with AI"
        ),
        market=Market(
            tam=50_000_000_000,
            market_growth_rate=0.35
        ),
        team=Team(
            founders=[{
                "name": "Jane Doe",
                "title": "CEO",
                "background": "Ex-Google AI, Stanford CS"
            }],
            team_size=25
        ),
        financials=Financials(
            revenue={"current": 2_000_000},
            expenses={"monthly": 150_000}
        ),
        fundraising=Fundraising(
            current_stage="series-a",
            target_amount=10_000_000
        )
    )

    # Get project assessment
    assessment = await advisor.assess_project(project)
    print(f"Investment Readiness Score: {assessment.overall_score}/100")
    print(f"Recommendation: {assessment.readiness_level}")

    # Generate valuation
    valuation = await advisor.valuate(project)
    print(f"Recommended Pre-Money Valuation: ${valuation.recommended_pre_money:,.0f}")

    # Generate pitch deck
    pitch_deck = await advisor.generate_pitch_deck(project)
    print(f"Generated {len(pitch_deck.slides)}-slide pitch deck")

    # Match investors
    matches = await advisor.match_investors(project, top_n=10)
    print(f"Found {len(matches)} matching investors")

if __name__ == "__main__":
    asyncio.run(main())

Features in Detail

1. Project Assessment

Evaluate investment readiness across 5 dimensions:

Team (25%): Founder experience, team completeness, key hires
Market (20%): TAM size, growth rate, competition
Product (20%): Stage, features, differentiation
Traction (20%): Revenue, users, growth metrics
Financials (15%): Unit economics, burn rate, projections

Output:

Overall score (0-100)
Investment readiness level (NOT-READY, NEEDS-IMPROVEMENT, READY, HIGHLY-READY)
Detailed dimension scores
Strengths and weaknesses
Actionable recommendations

assessment = await advisor.assess_project(project)

2. Valuation Analysis

Four professional valuation methods:

Scorecard Method - Compare to regional/stage benchmarks
Berkus Method - Value based on risk mitigation factors
Risk Factor Summation - Adjust for 12 risk categories
Comparable Companies - Revenue multiple (for revenue-stage companies)

Output:

Recommended pre-money and post-money valuations
Breakdown by method with weights
Suggested deal terms (raise amount, dilution %)
Assumptions and caveats

valuation = await advisor.valuate(project)

3. Pitch Deck Generation

Standard 12-slide structure:

Cover
Problem
Solution
Product Demo
Market Opportunity
Business Model
Traction
Competition
Go-to-Market Strategy
Team
Financial Projections
Funding Ask

Each slide includes key points, data visualization suggestions, and speaking notes.

pitch_deck = await advisor.generate_pitch_deck(project)

4. Business Plan Generation

Comprehensive business plan including:

Executive Summary
Company Overview
Problem & Solution
Market Analysis
Product/Service Description
Business Model
Go-to-Market Strategy
Competitive Analysis
Team
Financial Projections
Funding Request & Use of Funds
Exit Strategy

business_plan = await advisor.generate_business_plan(project)

5. Investor Matching

Intelligent matching algorithm considering:

Industry focus alignment
Stage focus (seed, series A, etc.)
Investment range (check size)
Geographic preference
Recent activity and portfolio relevance

Output:

Ranked list of matching investors
Match score explanation for each
Outreach strategy and prioritization
Contact approach recommendations

matches = await advisor.match_investors(project, top_n=20)
strategy = await advisor.generate_outreach_strategy(matches)

6. Investment Analysis (For Investors)

Professional investment memo including:

Executive summary with recommendation (PASS/MAYBE/PROCEED/STRONG-YES)
Investment highlights
Market opportunity analysis
Product assessment
Team evaluation
Competitive position
Financial analysis and valuation assessment
Key risks and mitigations
Due diligence checklist (40+ items across 8 categories)

analysis = await advisor.analyze_investment(project)

7. PDF Processing (Python Advantage)

Advanced PDF capabilities unavailable in TypeScript:

# Parse financial statements from PDF
financial_data = await advisor.parse_financial_pdf("financial_statement.pdf")

# OCR scanned documents
text = await advisor.ocr_pdf("scanned_business_plan.pdf", language='eng+chi_sim')

# Generate professional PDF reports
await advisor.generate_assessment_report(assessment, "report.pdf")
await advisor.generate_valuation_report(valuation, "valuation.pdf")
await advisor.generate_investment_memo(memo, "memo.pdf")

Use Cases

For Startup Founders

Scenario 1: Preparing for Series A

# Get complete fundraising package
advisor = FAAdvisor()
assessment = await advisor.assess_project(project)
valuation = await advisor.valuate(project)
pitch_deck = await advisor.generate_pitch_deck(project)
business_plan = await advisor.generate_business_plan(project)
investors = await advisor.match_investors(project)

Scenario 2: Quick self-assessment

# Just get readiness score
assessment = await advisor.assess_project(project)
print(f"Score: {assessment.overall_score}/100")
print("Recommendations:", assessment.recommendations)

Scenario 3: Valuation guidance

# Understand fair valuation range
valuation = await advisor.valuate(project)
print(f"Recommended valuation: ${valuation.recommended_pre_money:,.0f}")

For Investors

Scenario: Due diligence and deal evaluation

# Generate investment memo
advisor = FAAdvisor()
analysis = await advisor.analyze_investment(project)
print(f"Recommendation: {analysis.recommendation}")
print(f"Key Risks: {len(analysis.risks)}")
print(f"DD Checklist: {len(analysis.dd_checklist)} items")

Project Structure

openclaw-finance-analyst/
├── fa_advisor/                 # Main Python package
│   ├── advisor.py             # FAAdvisor main class
│   ├── types/                 # Pydantic data models
│   │   ├── project.py         # Project schema
│   │   ├── investor.py        # Investor schema
│   │   └── models.py          # Result models
│   ├── modules/               # Business logic modules
│   │   ├── assessment/        # Project assessor
│   │   ├── valuation/         # Valuation engine
│   │   ├── pitchdeck/         # Pitch deck generator
│   │   ├── matching/          # Investor matcher
│   │   └── analysis/          # Investment analyzer
│   ├── pdf/                   # PDF processing
│   │   ├── parser.py          # PDF text extraction
│   │   ├── financial_parser.py # Financial data extraction
│   │   ├── ocr.py            # OCR capabilities
│   │   └── generator.py       # PDF report generation
│   └── data/                  # Investor database
├── examples/                   # Usage examples
├── tests/                      # Test suite
├── output/                     # Generated documents
├── SKILL.md                    # OpenClaw skill definition
├── README.md                   # This file
└── requirements.txt            # Python dependencies

Configuration

Investor Database

Add your investor database in fa_advisor/data/investors/:

{
  "investors": [
    {
      "name": "Sequoia Capital",
      "type": "vc",
      "stage_focus": ["seed", "series-a", "series-b"],
      "industry_focus": ["enterprise-software", "fintech", "ai"],
      "check_size_min": 1000000,
      "check_size_max": 25000000,
      "geography": ["us", "global"],
      "recent_investments": ["Company A", "Company B"]
    }
  ]
}

Customization

# Custom assessment weights
advisor = FAAdvisor(
    assessment_weights={
        "team": 0.30,      # Default: 0.25
        "market": 0.25,    # Default: 0.20
        "product": 0.20,   # Default: 0.20
        "traction": 0.15,  # Default: 0.20
        "financials": 0.10 # Default: 0.15
    }
)

# Custom valuation method weights
valuation = await advisor.valuate(
    project,
    method_weights={
        "scorecard": 0.40,
        "berkus": 0.30,
        "risk_factor": 0.20,
        "comparable": 0.10
    }
)

Python vs TypeScript

Why Python for FA Advisor?

Feature	Python	TypeScript	Winner
PDF Text Extraction	⭐⭐⭐⭐⭐	⭐⭐	Python
PDF Table Extraction	⭐⭐⭐⭐⭐	❌	Python
OCR Capabilities	⭐⭐⭐⭐⭐	❌	Python
PDF Report Generation	⭐⭐⭐⭐⭐	⭐⭐	Python
Data Analysis (Pandas/NumPy)	⭐⭐⭐⭐⭐	⭐⭐	Python
Machine Learning	⭐⭐⭐⭐⭐	⭐	Python
Type Safety	⭐⭐⭐⭐ (Pydantic)	⭐⭐⭐⭐⭐	Both Good
Runtime Validation	⭐⭐⭐⭐⭐ (Pydantic)	⭐⭐⭐	Python

Conclusion: For FA Advisor with heavy PDF processing needs, Python is the clear choice.

API Reference

FAAdvisor Class

class FAAdvisor:
    """Main FA Advisor class providing all advisory services."""

    async def assess_project(self, project: Project) -> Assessment:
        """Evaluate project investment readiness."""

    async def valuate(self, project: Project, methods: List[str] = None) -> Valuation:
        """Calculate startup valuation using multiple methods."""

    async def generate_pitch_deck(self, project: Project) -> PitchDeck:
        """Generate professional pitch deck outline."""

    async def generate_business_plan(self, project: Project) -> BusinessPlan:
        """Generate comprehensive business plan."""

    async def match_investors(self, project: Project, top_n: int = 20) -> List[InvestorMatch]:
        """Find and rank matching investors."""

    async def generate_outreach_strategy(self, matches: List[InvestorMatch]) -> OutreachStrategy:
        """Generate investor outreach strategy."""

    async def analyze_investment(self, project: Project) -> InvestmentAnalysis:
        """Generate investment analysis for investors."""

    # PDF Processing
    async def parse_financial_pdf(self, pdf_path: str) -> FinancialData:
        """Extract financial data from PDF."""

    async def ocr_pdf(self, pdf_path: str, language: str = 'eng') -> OCRResult:
        """Perform OCR on scanned PDF."""

    async def generate_assessment_report(self, assessment: Assessment, output_path: str):
        """Generate PDF assessment report."""

Testing

# Run all tests
python3 -m pytest

# Run specific test
python3 test_complete.py

# Run example
python3 example_python.py

Expected output from test_complete.py:

✅ Test 1/6: Project Assessment - PASSED
✅ Test 2/6: Valuation Analysis - PASSED
✅ Test 3/6: Pitch Deck Generation - PASSED
✅ Test 4/6: Business Plan Generation - PASSED
✅ Test 5/6: Investor Matching - PASSED
✅ Test 6/6: Investment Analysis - PASSED
🎊 All tests passed!

Limitations & Disclaimers

What FA Advisor Can Do

✅ Provide data-driven analysis and recommendations ✅ Generate professional documentation templates ✅ Offer valuation estimates based on established methods ✅ Match with investors based on criteria

What FA Advisor Cannot Do

❌ Provide legal or accounting advice ❌ Guarantee funding success ❌ Replace human judgment and due diligence ❌ Access real-time market data (uses configured database) ❌ Make investment decisions for you

Important: Valuations are estimates based on models and assumptions. Always validate with professional advisors and market research.

Roadmap

Version 0.2.0 (Q2 2026)

DCF (Discounted Cash Flow) valuation method
Integration with Crunchbase API
Multi-language support (Chinese, Spanish)
Enhanced ML-based investor matching

Version 0.3.0 (Q3 2026)

Canvas integration for visual pitch decks
Voice interaction for pitch practice
Real-time market data integration
Collaborative features (team editing)

Version 1.0.0 (Q4 2026)

Web UI dashboard
Mobile app
Premium investor database
SaaS deployment option

Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

Areas where we need help:

Investor database expansion
Additional valuation methods
Test coverage
Documentation improvements
Internationalization

Support

Documentation: See SKILL.md for AI agent instructions
Issues: Report bugs or request features on GitHub Issues
Quick Start: See QUICKSTART.md for 5-minute getting started guide

License

MIT License - See LICENSE for details

Acknowledgments

OpenClaw community for the skill framework
Open source Python ecosystem (Pydantic, pdfplumber, tesseract, etc.)
Financial advisory methodologies from industry best practices

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