AI Lead Generation Tool

Founder's Project · 2025 · 2 min read

Built an intelligent lead generation platform that automates prospect discovery and qualification using AI, increasing client conversion rates by 3x.

Overview

Designed and developed a full-stack AI-powered lead generation tool for a B2B SaaS client. The platform scrapes, enriches, and scores leads using machine learning models, then delivers qualified prospects directly to sales teams through an intuitive dashboard.

Problem

The client's sales team was spending 60% of their time manually researching and qualifying leads, leaving little time for actual selling. They needed an automated system that could find, enrich, and rank prospects at scale.

Constraints

  • Must handle 10,000+ leads per day without performance degradation
  • Real-time scoring and ranking with sub-second response times
  • GDPR-compliant data handling and storage
  • Integration with existing CRM systems

Approach

We built a MERN stack application with a React frontend styled with Tailwind CSS, served behind Nginx as a reverse proxy and load balancer. The backend uses Node.js/Express with MongoDB for flexible lead data storage, and integrates OpenAI APIs for lead scoring and email copy generation.

Key Decisions

MERN stack with Tailwind CSS

Reasoning:

MongoDB's flexible schema handles varied lead data structures perfectly. React + Tailwind enabled rapid UI iteration. Express provided the API backbone for AI service integration.

Alternatives considered:
  • Django + PostgreSQL — stronger ORM but less flexible for varied lead schemas
  • Next.js — considered but client needed SPA behavior for the dashboard

Nginx as reverse proxy

Reasoning:

Handles SSL termination, load balancing across Node.js workers, and serves the static React build efficiently. Also provides rate limiting for the scraping endpoints.

Alternatives considered:
  • Caddy — simpler config but less battle-tested at scale
  • Direct Node.js serving — insufficient for production traffic patterns

Tech Stack

  • React
  • Tailwind CSS
  • Node.js
  • Express
  • MongoDB
  • Nginx
  • OpenAI API
  • Redis
  • Docker

Result & Impact

  • Reduced by 80%
    Lead qualification time
  • 3x improvement
    Conversion rate
  • 10,000+
    Leads processed daily

The sales team shifted from spending most of their day researching prospects to focusing on high-quality conversations. The AI scoring model improved over time with feedback loops, making the tool increasingly accurate.

Learnings

  • MongoDB's flexible schema was critical for handling inconsistent lead data from multiple sources
  • Nginx rate limiting was essential to prevent abuse of scraping endpoints
  • Building feedback loops into AI scoring models pays dividends over time

Technical Deep Dive

The architecture follows a microservices-inspired approach within a monorepo. The scraping service runs as background workers, feeding enriched data into MongoDB. The scoring service calls OpenAI’s API to classify and rank leads, caching results in Redis for the dashboard’s real-time queries.

Nginx handles SSL termination, load balances across multiple Express workers, and serves the optimized React build as static assets. This setup handles burst traffic during automated scraping runs without impacting dashboard responsiveness.

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