AI Lead Generation & Prospect Intelligence Automation
Developed a fully automated lead generation system that discovers businesses, enriches contact information, and creates qualified prospect lists without manual research.
The workflow collects business data from online sources, extracts company information, websites, email addresses, phone numbers, and social profiles, enriches each lead using AI, removes duplicates, and stores everything in a structured CRM-ready database.
This automation dramatically reduces hours of manual prospecting while providing high-quality leads ready for outreach.
Features
Automated business discovery
Website and contact extraction
Email and social profile collection
AI-powered lead enrichment
Data cleaning and deduplication
CRM-ready output
Scalable workflow for thousands of leads
Tech Stack
n8n • AI Models • Google Maps • Serper API • Google Sheets • Airtable
n8n is absolute gold for orchestrating lead pipelines like this! Connecting Gmail parsing with LLM qualification saves teams dozens of hours weekly. As someone who builds custom agentic lead automation workflows, seeing clean visual architectures like this is super satisfying. Top-tier build, Talha!.
LumaClean was losing hours to manual quoting, scheduling, confirmations, rescheduling, and follow-up. LumaFlow turns that entire process into one automated booking workflow.
I built LumaFlow for a fictional Chicago cleaning business, LumaClean.
The problem was simple: too much back-and-forth just to turn a customer inquiry into a confirmed booking.
I designed and built an enterprise AI automation system using n8n, AI agents, RAG, Redis, and PostgreSQL to automate complex business workflows, improve decision-making, and create scalable AI-powered operations.
The system uses n8n as the automation orchestration layer, where incoming business events trigger workflows that route tasks to specialized AI agents. A RAG pipeline retrieves relevant knowledge from business data sources, allowing AI agents to generate accurate, context-aware responses and decisions. Redis handles queue-based processing for high-volume tasks, while PostgreSQL stores structured data, workflow history, and audit records.
The automation architecture connects multiple technologies including n8n, OpenAI API, AI Agents, RAG pipelines, Vector Databases, Redis, PostgreSQL, APIs, Webhooks, Slack integrations, Docker, and Python services to create reliable enterprise workflows.
The solution helps businesses reduce manual operations, automate repetitive processes, improve response times, maintain better data accuracy, and scale AI workflows securely. It includes monitoring, validation, error handling, and human approval flows to ensure reliable production usage.