Turn Reddit & Quora Posts into Paying Users – Meet Leadhunt
Turn Reddit & Quora Posts into Paying Users – Meet Leadhunt

Inspiration

I wanted to solve the biggest pain point for SaaS founders: finding high-intent prospects who actually need their solution. Traditional lead generation is expensive, time-consuming, and often targets the wrong people. I thought of an AI-powered platform that could automatically discover people expressing genuine problems on social platforms and help craft personalized outreach messages that actually convert.

What it does

LeadsHunt is a comprehensive AI-powered lead generation platform specifically designed for SaaS companies. The platform operates across multiple stages of the lead generation process: SaaS Profile Setup: Users start by creating detailed profiles of their SaaS business, including product description, target keywords, category, and pain points they solve. This forms the foundation for AI-powered lead discovery.

Multi-Platform Lead Discovery: The platform searches across Reddit, Quora and other platforms to find people actively expressing problems that the user's SaaS can solve. Real-time scraping combined with AI analysis identifies high-intent prospects based on engagement scores, urgency levels, and pain point matching.
AI Analysis & Scoring: Each discovered lead is analyzed by Google Gemini AI to assess intent level (high/medium/low), identify specific pain points, and provide strategic insights. The AI generates engagement scores and recommends the best approach for outreach.
AI Message Composer: Beyond just finding leads, the platform generates personalized outreach messages tailored to each prospect's specific situation. Users can specify audience, tone, platform, and keywords to create compelling messages that address the prospect's exact pain points.
Lead Management: Users can save promising leads, organize them by platform or intent level, and track their outreach efforts. The dashboard provides analytics on lead discovery performance, message generation, and conversion tracking.
Freemium Subscription Model: Free users get 2 searches per month, while Pro subscribers ($15/month) get 500 searches, advanced AI responses, unlimited saved leads, and priority support. The platform includes complete Stripe integration with automatic subscription management.

How to access it

you can use the below credentials Demo account: test@example.com password: 12345678

How I built it

The platform is built on a modern, scalable architecture using Next.js 15 as the frontend framework with React 18, TypeScript, and Tailwind CSS for the UI. The entire application is designed with a dark theme optimized for professional use. Frontend Architecture: I used Zustand for state management, handling lead discovery, navigation, and user preferences. Framer Motion provides smooth animations throughout the interface. The component architecture is modular with separate modules for authentication, dashboard, lead discovery, AI composer, and user management. Backend & Database: Supabase serves as the backend-as-a-service, providing PostgreSQL database, real-time subscriptions, authentication, and edge functions. The database schema includes user profiles, SaaS profiles, composed messages, and subscription management tables with proper Row Level Security (RLS) policies. AI Integration: Google Gemini 2.0 Flash powers the entire AI layer. I built separate services for Reddit scraping, Quora API integration, and AI lead generation for other platforms. The AI analyzes posts to extract pain points, assess purchase intent, and generate contextual responses. Real-time Lead Discovery: For Reddit, I built a service that uses the public JSON API to search for relevant posts based on AI-generated search queries. For Quora, I implemented a server-side API to handle CORS restrictions. The AI generates platform-specific search queries optimized for finding people with specific problems. Payment Integration: Complete Stripe integration with checkout sessions, billing portal, webhook handling, and automatic subscription management. Edge functions handle payment verification, subscription updates, and usage tracking. Deployment: The application uses Supabase Edge Functions for serverless backend operations, with Stripe webhooks for real-time subscription management and search limit enforcement.

Tech stacks i used

Bolt **- Used for rapid prototyping and development acceleration **next.js - React framework for production-grade web applications react - Frontend UI library with hooks and modern patterns typescript - Type-safe development and better code reliability tailwindcss - Utility-first CSS framework for responsive design Supabase - Backend-as-a-service with PostgreSQL and edge functions google-gemini-ai - Large language model for lead analysis and message generation Stripe- Payment processing and subscription management framer-motion - Animation library for smooth user interactions zustand - Lightweight state management for React lucide-react - Beautiful, consistent icon library react-hook-form - Form handling with validation cheerio - Server-side HTML parsing for web scraping recharts - Data visualization for analytics dashboard

Challenges we ran into

CORS and API Limitations: Many social platforms restrict direct API access or have CORS limitations. I had to implement server-side proxies and fallback systems to ensure reliable lead discovery even when external APIs were unavailable.
AI Prompt Engineering: Getting consistent, high-quality results from Gemini required extensive prompt optimization. I had to balance between getting realistic-sounding leads while ensuring they matched the user's specific SaaS requirements. The challenge was making AI-generated leads indistinguishable from real social media posts.
Rate Limiting & Subscription Logic: Implementing fair usage policies while providing a smooth user experience was complex. I built a comprehensive system that tracks monthly usage, resets counts automatically, and enforces limits without disrupting the user flow.
State Management Complexity: Managing state across lead discovery, saved leads, user profiles, and subscription status required careful architecture. I used Zustand stores with proper hydration handling to prevent SSR mismatches.
Real-time Data Consistency: Ensuring that lead discovery, saving, and subscription status stayed in sync across the application required careful state management and optimistic updates with error handling.

Accomplishments that I'm proud of

I built a complete, production-ready SaaS platform with real AI capabilities that genuinely solves a problem for SaaS founders. The platform successfully combines real social media data with AI analysis to create a tool that can actually help businesses find and engage with prospects. The AI message composer generates contextually relevant outreach messages that sound natural and helpful rather than spammy. The multi-platform approach means users can discover leads across the entire social web, not just one platform. The subscription system is robust with proper webhook handling, automatic upgrades/downgrades, and seamless billing portal integration. The free tier provides genuine value while the Pro tier offers compelling upgrades.

What I learned

Building effective AI applications requires as much focus on prompt engineering and data quality as it does on technical architecture. The quality of lead discovery depends heavily on how well you can generate search queries that find people with genuine problems. I also learned the importance of building for both technical users (who want detailed controls) and non-technical users (who want simple, automated solutions). The platform needs to be powerful enough for experienced marketers while remaining accessible to first-time founders.

What's next for LeadHunt

I plan to expand platform coverage to include more specialized communities like ProductHunt, IndieHackers, Linkedin and industry-specific forums. The AI analysis can be enhanced with sentiment analysis and competitive intelligence features. I want to add email automation capabilities, allowing users to automatically send generated messages through email sequences. Integration with popular CRM systems would help users track leads through their entire sales funnel. Advanced analytics including conversion tracking, A/B testing of message variations, and ROI calculations would help users optimize their lead generation efforts. A mobile app would enable lead discovery and outreach on the go.