From Spreadsheet Chaos to Production-Ready Cap Table in One Day

How AI helped me build enterprise-grade equity management software in 24 hours, managing 47 stakeholders and 254,647 shares

The Challenge: Real-World Equity Complexity

As a CEO managing equity across 47 shareholders and 254,647 shares, I was drowning in spreadsheet chaos. Version control nightmares, manual calculations, and the constant fear of human error had me spending hours on what should be automated tasks.

Managing equity isn’t just about pretty charts—it’s about handling real complexity:

• 47 stakeholders across founders, investors, and employees
• Multiple share classes (Equity and CCPS)
• Stock option grants with complex vesting schedules
• Transaction history that needs to be bulletproof
• Role-based access where everyone sees only what they should

The existing tools either cost $500+/month or were built for enterprises with dedicated finance teams. I needed something lean but powerful—exactly what a startup CEO would build for their own use.

Then I decided to practice what I preach about AI-driven productivity: instead of researching existing solutions for months, I’d build exactly what I needed in a single day.

The result? A production-ready cap table management system that went from concept to deployment in under 24 hours.

The AI-Powered Development Approach

Instead of weeks of planning and architecture documents, I started with a simple prompt to Claude: “Help me build a cap table management system.” What happened next changed how I think about software development.

Phase 1: Architecture in Minutes, Not Days

Within 30 minutes, I had:

• Complete system architecture with database schema
• Technology stack recommendations (React + Node.js + PostgreSQL)
• Security considerations including JWT authentication
• Deployment strategies with cost analysis

The AI didn’t just suggest generic patterns—it understood the specific requirements of equity management and recommended Prisma ORM for the complex relational queries cap tables require.

Phase 2: From Code to Running System in Hours

Here’s where it gets interesting. Instead of writing boilerplate code for hours, I focused on the business logic while AI handled the scaffolding:

Backend Development (2 hours):

• Express.js API with TypeScript
• Prisma database schema with 8 interconnected tables
• JWT authentication with role-based permissions
• Real-time equity calculations and vesting logic

Frontend Development (3 hours):

• React dashboard with Tailwind CSS
• Interactive charts using Recharts
• Admin panel for complete shareholder management
• Responsive design that works on mobile

Integration & Testing (1 hour):

• Docker containerization for easy deployment
• Database seeding with real cap table data
• End-to-end testing of critical workflows

The Real Win: Business Logic, Not Boilerplate

The most powerful aspect wasn’t code generation—it was having AI as a reasoning partner for complex business logic:

Vesting Calculations: Instead of googling “how to calculate ESOP vesting,” I described our specific vesting schedule and got production-ready algorithms that handle cliff periods, acceleration events, and partial vesting.

Security Implementation: Rather than copy-pasting auth code, I explained our access control needs and got a complete JWT strategy with refresh tokens and role-based permissions.

Database Design: Instead of struggling with foreign keys and relationships, I described our equity structure and got a normalized schema that prevents data inconsistencies.

From CSV Hell to Real-Time Dashboard

The transformation is striking. Before:

• Manual calculations in Google Sheets with constant errors
• Version control nightmares with multiple copies floating around
• No audit trail of who changed what when
• Hours of prep for every board meeting

After:

• Real-time equity calculations that update instantly
• Complete transaction history with timestamps and audit trails
• Role-based dashboards where everyone sees exactly what they need
• One-click CSV exports for external reporting

Technical Highlights

For the developers reading this, here’s what impressed me most about the technical implementation:

Real-time Ownership Calculations:

const updateOwnershipPercentages = async (shareClassId: string) => {
  const totalShares = await prisma.equityHolding.aggregate({
    where: { shareClassId },
    _sum: { shares: true }
  });
  
  // Update all percentages atomically
  await prisma.$transaction(async (tx) => {
    const holdings = await tx.equityHolding.findMany({
      where: { shareClassId }
    });
    
    for (const holding of holdings) {
      await tx.equityHolding.update({
        where: { id: holding.id },
        data: { 
          percentageOwnership: (holding.shares / totalShares._sum.shares) * 100 
        }
      });
    }
  });
};

The system handles complex scenarios like:

• Atomic transactions ensuring data consistency
• Automatic vesting calculations that run in background jobs
• Optimistic UI updates for instant feedback
• Progressive web app features for mobile access

Key Features Delivered

Core Functionality

• 🔍 Real-time Equity Visibility - All stakeholders can view their current holdings
• 📈 Transaction Tracking - Complete history of buy/sell/grant/exercise transactions
• ⏰ ESOP Vesting Management - Automated vesting calculations with customizable schedules
• 🏷️ Share Class Management - Support for Common, Preferred, and Option pools
• 👥 Role-based Access Control - Separate views and permissions for Admins and Stakeholders

Advanced Features

• 📊 Interactive Dashboard - Beautiful charts and visualizations for ownership breakdown
• 📋 CSV Export - Export cap table data for external analysis
• 🔐 Secure Authentication - JWT-based auth with refresh tokens
• 📱 Responsive Design - Works seamlessly on desktop, tablet, and mobile
• 🚨 Admin Panel - User management, share class configuration, and system oversight

Deployment: From Localhost to Production

Getting to production was surprisingly smooth. The AI had already configured:

• Docker containerization for consistent environments
• Environment variable management for different stages
• Database migrations that can run safely in production
• Health checks and monitoring endpoints

I went with Railway for deployment—a platform I’d never used before but that AI recommended for its simplicity. Total deployment time: 15 minutes.

The Business Impact

Here’s what matters most to me as a CEO:

Time Savings: What used to take 4-5 hours of spreadsheet work now takes 10 minutes of data entry.

Accuracy: Zero calculation errors since launch, compared to weekly mistakes in manual spreadsheets.

Stakeholder Experience: Investors and employees can see their equity anytime, reducing “what’s my ownership?” emails by 90%.

Board Prep: Cap table reports that used to take half a day now generate instantly.

The ROI Calculation

Traditional approach:

• Research existing tools: 20 hours
• Evaluate and negotiate: 10 hours
• Implementation and training: 15 hours
• Monthly cost: $500+
• Total first-year cost: $11,000+ and 45 hours

AI-powered approach:

• Development time: 8 hours
• Deployment and testing: 2 hours
• Monthly hosting: $20
• Total first-year cost: $240 and 10 hours

That’s a 98% cost reduction and I own the entire system.

Lessons for Other CEOs

This project reinforced three key principles about AI-driven productivity:

1. Start with Business Problems, Not Technology I didn’t start by learning React or PostgreSQL. I started by describing the business problem and let AI guide the technical decisions.

2. Focus on Your Unique Value My value wasn’t writing authentication middleware—it was understanding our specific equity structure and business rules. AI handled the commodity code while I focused on what makes our situation unique.

3. Ship Fast, Iterate Faster Instead of months of planning, I shipped a working system in one day. Now I can iterate based on real usage patterns instead of theoretical requirements.

What’s Next

The system is already handling our complete cap table, but I’m excited about the enhancement possibilities:

• Automated vesting notifications via email
• Document management for stock certificates
• Advanced analytics for ownership modeling
• API integrations with our accounting system

Each of these features would have been a major project before. Now they’re weekend experiments.

The Broader Lesson

This isn’t really a story about building software—it’s about fundamentally changing how we approach complex business problems.

Instead of asking “What tools exist to solve this?” I’m now asking “What would the perfect solution look like?” and using AI to bridge the gap between vision and reality.

For CEOs and founders, this changes everything. We’re no longer limited by development resources or vendor roadmaps. If you can clearly articulate a business problem, you can probably build the solution.

The question isn’t whether AI will transform how we build software—it’s whether you’ll be among the first to harness this productivity multiplier or still be researching options when your competitors ship.

Tech Stack: React + TypeScript + Node.js + PostgreSQL + Docker
Timeline: 1 day from concept to production
Lines of code: ~8,000 (mostly generated, then customized)
Current status: Managing 47 stakeholders and 254,647 shares in production

This post is part of my series on “10xing with AI” - practical examples of how builders can leverage AI to accelerate their outcomes.