LunchLab

Inspiration

Every entrepreneur has that moment—a spark of an idea that could change everything. But too often, great ideas die in the "what if" stage because founders lack the tools to validate them quickly. We built LaunchLab after witnessing countless aspiring entrepreneurs waste months (and sometimes years) building products nobody wanted. We wanted to democratize startup validation by giving anyone instant, AI-powered feedback on their ideas before they invest time and money.

What it does

LaunchLab is an AI-powered startup idea validator that provides instant, actionable feedback on business concepts. Users describe their startup idea, and our platform analyzes it across multiple dimensions:

• Smart Scoring System: Evaluates ideas on a 0-100 scale based on problem-solution fit, market timing, competitive advantage, and feasibility
• Risk Assessment: Identifies 3 specific risks and challenges the startup might face
• Strength Identification: Highlights 3 key advantages and opportunities
• Actionable Recommendations: Provides clear guidance (BUILD, REFINE, or PIVOT) with detailed next steps
• Rate Limiting & Analytics: Built-in usage tracking with 10 free analyses per day to prevent abuse
• History Tracking: Authenticated users can save and review past analyses
• PDF Reports: Download professional validation reports to share with co-founders or investors
• Admin Dashboard: Comprehensive analytics including AI usage monitoring, cost tracking, and user insights

How we built it

LaunchLab leverages a modern, scalable tech stack:

Frontend: React with TypeScript, Vite for blazing-fast builds, Tailwind CSS for a beautiful, responsive design, and Shadcn UI for consistent components

Backend: Lovable Cloud (Supabase) providing:

• PostgreSQL database with Row-Level Security (RLS) policies
• Edge Functions for serverless AI processing
• Real-time authentication and user management
• Automated database migrations

AI Integration: Lovable AI Gateway accessing Google Gemini 2.5 Flash for intelligent idea analysis with structured output via tool calling

Security & Monitoring:

• Comprehensive input validation on both client and server
• Rate limiting system (10 analyses/day per user or IP)
• Usage logging with token and cost tracking
• Admin analytics dashboard with real-time monitoring
• Proper RLS policies protecting user data

Key Architecture Decisions:

• Edge functions for AI processing (keeping API keys secure)
• Database-driven rate limiting for scalability
• Cost tracking per request (~$0.0001-0.0003 per analysis)
• Separation of authenticated and anonymous user flows

Challenges we ran into

1. Security Hardening: Initially discovered our admin stats were publicly accessible through a database view. We solved this by converting it to a SECURITY DEFINER function with proper role-based access control, ensuring only admin users could access sensitive business metrics.

2. Rate Limiting Architecture: Implementing fair rate limiting for both authenticated and anonymous users was complex. We needed to track by user_id for logged-in users and IP address for anonymous users, while ensuring the system remained performant with proper database indexing.

3. AI Cost Management: Without proper monitoring, AI usage could spiral out of control. We built a comprehensive tracking system that logs every request with token usage and estimated costs, giving admins real-time visibility into service consumption.

4. Input Validation: Balancing security with user experience was tricky. We needed to prevent prompt injection and abuse while not being overly restrictive on legitimate use cases. We implemented multi-layered validation with clear error messages.

5. Type Safety with RPC Calls: TypeScript type inference with Supabase RPC functions required explicit type assertions to ensure type safety while calling database functions from edge functions.

Accomplishments that we're proud of

🎯 Production-Ready Security: We didn't just build a demo—we built a secure, production-ready application with comprehensive RLS policies, input validation, and proper authentication flows.

💰 Cost-Conscious Design: Built-in usage tracking and rate limiting means LaunchLab can scale sustainably without unexpected AI service bills.

📊 Data-Driven Insights: The admin analytics dashboard provides unprecedented visibility into AI usage patterns, costs, and potential abuse—crucial for any AI-powered SaaS.

⚡ Blazing Fast: Edge functions and optimized queries mean users get AI-powered validation results in seconds, not minutes.

🔒 Privacy-First: Every user only sees their own analysis history, and admin access is properly gated behind server-side role checks.

📱 Beautiful UX: Responsive design with smooth animations and clear visual feedback makes startup validation feel premium and professional.

What we learned

AI Integration Best Practices: We learned that calling AI services directly from the client is a security nightmare. Edge functions are essential for protecting API keys and implementing proper rate limiting.

Database Security is Non-Negotiable: RLS policies aren't optional—they're the foundation of secure multi-tenant applications. We learned to think "security-first" when designing database schemas.

Monitoring Matters: You can't optimize what you don't measure. Building comprehensive usage analytics from day one gave us insights that would have taken weeks to implement retroactively.

Rate Limiting is User Experience: Good rate limiting isn't just about preventing abuse—it's about setting clear expectations. Showing users "7 of 10 analyses remaining" turns a restriction into transparency.

TypeScript Types Prevent Bugs: Strong typing with proper interfaces caught numerous bugs before they reached production, especially around database queries and AI response parsing.

What's next for LaunchLab

🚀 Short-term Goals:

• Premium Tier: Unlimited analyses for paid subscribers with Stripe integration
• Enhanced AI Analysis: Add competitor analysis and market size estimation
• Email Reports: Automated weekly summaries of validation trends
• Public Idea Board: Optional public sharing of validated ideas (with user permission)

📈 Medium-term Goals:

• Team Collaboration: Multiple users can validate ideas together with shared workspaces
• Integration Hub: Connect with tools like Notion, Slack, and Trello to share validation reports
• Advanced Analytics: Machine learning to predict which ideas are most likely to succeed based on historical data
• Multi-language Support: Validate ideas in Spanish, French, German, and more

🌟 Long-term Vision:

• Validation Marketplace: Connect validated ideas with potential co-founders, investors, and service providers
• Follow-up Tracking: Help founders track progress from idea to launch with milestone management
• Community Features: Forums and discussion boards for entrepreneurs to refine ideas together
• AI Mentorship: Personalized guidance throughout the entire startup journey, not just validation

LaunchLab started as an idea validator, but we envision it becoming the complete platform for aspiring entrepreneurs—from that first "what if" moment all the way to product-market fit.

Built With

• lovable