Automated AI Lead Generation with Open Source Worker

Описание и применение

Автор описывает использование открытого AI-инструмента 'Lead Researcher' для генерации 2000 квалифицированных лидов за выходные без затрат на инструменты. Система работает локально через AI-кодеры (Claude Code, Codex CLI), исследует публичные данные, проверяет соответствие ICP и экспортирует CSV с доказательствами.

Задача

Traditional lead generation requires significant human effort, expensive tools, or subscriptions to research companies, identify decision-makers, verify data, and score leads against an Ideal Customer Profile (ICP).

Результат

Generated 2,000 qualified leads over a weekend with zero spend on lead gen tools. The system automatically researched, verified, and scored leads based on a strict ICP, exporting a CSV with evidence for each lead.

Рабочий процесс

• Install the 'Lead Researcher' worker into an existing AI coding tool (Claude Code, Codex CLI, or OpenCode).
• Define the Ideal Customer Profile (ICP) in a plain-language file.
• Issue a command to the worker, e.g., 'Get 2000 leads for me based on my ICP and don't stop until it is done.'
• The worker researches public web data, identifies people, verifies data, and scores leads against the ICP.
• The worker rejects duplicates, guessed emails, and vague fits based on strict quality gates.
• The worker exports a 'leads.csv' file containing the lead list, sources, and justifications.
• Optionally, use the hands-free scheduler to run the worker periodically in the background.

Доказательства

My AI company generated 2,000 qualified leads over the weekend, while I slept. Zero dollars spent on lead gen tools.

Источник

A lead only counts if it scores above the bar AND cites the ICP file as evidence.

Источник