AI Lead Finder

Finding good prospects manually is repetitive, slow, and difficult to scale.

Traditional lead generation often requires searching for companies, checking their websites, identifying relevant contacts, finding professional email addresses, researching the company, and then writing a personalised message for every prospect.

The work is valuable, but much of the process is repetitive enough to be handled by automation.

Built an n8n pipeline that connects prospect discovery to personalised outreach.

Discovery tools find matching companies, enrichment identifies company information and contacts, and an AI model reads the gathered context before generating a subject line and personalised outreach message.

The lead, contact, and outreach data stays structured for Gmail and Google Sheets rather than being scattered across manual notes.

Discovery → enrichment → intelligence → outreach.

The workflow moves a lead through several stages before it reaches the final outreach step.

The automation in action.

The workflow connects research, enrichment, AI reasoning, and outreach preparation into a single automated pipeline.

AUTOMATION WORKFLOW

The automation pipeline connecting lead discovery, enrichment, AI processing, and outreach preparation.

GENERATED OUTREACH

An example of the personalised outreach generated from the lead's collected context.

What was built.

• Automated company discovery and web research.
• Lead extraction, parsing, and structured storage.
• Deduplication and company-level enrichment.
• Contact discovery and professional email identification.
• AI context analysis for determining useful personalisation.
• AI-generated personalised subject lines and outreach messages.
• Structured lead tracking through Google Sheets.
• Gmail preparation for the resulting outreach.

AI is used for context, not just text generation.

The AI layer receives information gathered during the research and enrichment stages before generating the outreach. This gives the message actual company and contact context instead of relying on generic templates.

The system also produces structured output so the generated information can move cleanly into the rest of the workflow.

Key decisions and compromises.

• Automation vs review — the system automates research and preparation while keeping the resulting lead data visible for review.
• Broad discovery vs lead quality — finding more companies is less useful if enrichment and qualification cannot keep up.
• AI flexibility vs structured output — AI is given room to reason about context, while its output is constrained into fields the workflow can reliably process.
• Multiple APIs — external services make enrichment much more capable but also introduce dependencies, limits, and potential failure points.

A complete automated path from prospect discovery to personalised outreach, with the lead data staying structured and reviewable.

The system discovers potential companies, enriches their information, identifies relevant contacts, evaluates available context, and generates personalised outreach for the resulting leads.

Lead information and workflow status remain structured in Google Sheets, while the generated outreach can be prepared for Gmail.

• Lead generation becomes much more useful when discovery, enrichment, and outreach are treated as one system.
• AI personalisation is significantly more useful when the model receives real research context before generating a message.
• Structured intermediate data makes complex automation workflows easier to debug and maintain.
• External APIs provide powerful capabilities but need careful handling because every dependency can become a failure point.