AI needs structured lead data, not messy spreadsheets
AI lead generation works best when the model or workflow receives consistent fields: business type, location, website, email, phone, social profiles, reviews, and source URLs. Without structure, agents spend more time cleaning data than qualifying leads.
Margo turns local business prospecting into a repeatable data source that can feed outreach workflows, CRMs, and AI agents.
From prompt to prospect list
Use Margo to search for a niche like dentists in Austin, gyms in London, restaurants in Manchester, or SaaS founders in the United States. The resulting data can be exported or requested through the API.
That makes it easier to build agents that research a market, create a prospect list, score the fit, and hand off qualified leads to a human or outreach system.
Designed for practical outbound teams
Margo is not just a chatbot wrapper. It is a lead data layer for teams that need real businesses, contact details, and qualification signals. Agencies, founders, sales teams, and automation builders can all use the same workflow.
Start manually in the dashboard, then move repeatable campaigns into the API when the workflow is proven.
Frequently Asked Questions
How does AI lead generation work with Margo?
Margo lets teams describe a market, search local business data, enrich matching leads, and export structured records that can be used by sales teams, CRMs, workflow automations, or AI agents.
Can AI agents use Margo?
Yes. Margo publishes API documentation and a skill manifest so agent frameworks and workflow tools can discover the lead generation API.
What makes local business AI lead generation different?
Local business prospecting needs location, category, contact, website, social, and review data. Margo combines these fields so AI workflows can qualify prospects with more context.