AI Agent Automation for Marketing, Sales, and Sourcing

Before building Pomos AI, I received inquiries from more than 200 clients through Fiverr and Upwork. Many of them were asking for the same thing in different language: they wanted to remove repetitive manual work from marketing, sales, sourcing, research, and operations.

I studied their needs, mapped their workflows, and helped them build initial automation workforces. Some clients needed lead discovery. Some needed outbound research. Some needed sales support, content workflows, sourcing pipelines, or internal systems that could coordinate repeatable work.

After working through enough client projects, I realized many of those automation patterns could be useful for many other businesses. The details changed by industry, but the underlying needs were similar: find the right businesses, understand what they do, decide what work should happen next, and execute that work with reliable AI agents.

That insight became the foundation for Pomos AI. Instead of building every automation as a completely custom one-off project, I wanted to create an agent workforce that could apply the same core capabilities across marketing, sales, sourcing, and business development workflows.

Pomos AI is the product version of that client experience. It has access to more than 1.5 billion B2B contacts, more than 200 agents and teams, and more than 50,000 skills. The goal is to discover businesses, understand opportunities, and deliver work directly to the business.

That means the system is not only a chatbot or a simple prompt wrapper. It is designed as an automation workforce: agents that can research, source, qualify, enrich, write, analyze, and coordinate business tasks across different use cases.

Marketing, sales, and sourcing all depend on repeated judgment over large amounts of information. Teams need to find the right companies, understand context, prioritize targets, create relevant outreach, and keep the workflow moving. AI agents can help when they are connected to the right data, skills, and execution paths.

My approach is to build these systems as practical software first. The automation needs to be observable, useful, and connected to real business outcomes. Pomos AI is one example of how client automation work can evolve into a broader agentic platform for businesses.