Building AstridOS - AI-Powered CRM Automation

The Vision

I wanted to build a CRM that didn't just store leads and contacts, but actively helped manage the sales pipeline through AI automation. The goal was to create AI agents that could handle routine tasks like outreach, follow-ups, and pipeline management.

Frontend Development

I built the frontend with Next.js/TypeScript, focusing on a clean, intuitive interface. The dashboard needed to show leads, pipeline status, and AI agent activity all in one view. I created components for lead management that made it easy to see the status of each contact at a glance.

Backend Architecture

The backend uses Node/Express and Next API routes. I designed the API to support both traditional CRM operations and AI agent interactions. This required careful planning of how AI agents would access and update lead data.

AI Agent System

The most complex part was building the foundation for AI agents. I created a system where agents could be configured to handle specific tasks: sending follow-up emails, updating lead statuses, and even initiating outreach campaigns.

I wrote comprehensive documentation for setting up and updating AI agents, which was crucial for maintaining and improving the system over time.

Deployment and Maintenance

I set up deployment to Railway and created scripts for cleaning up old tasks and managing the AI agent queue. This automated maintenance was essential for keeping the system running smoothly.

Authentication and Security

I implemented secure authentication and ensured that all AI agent actions were logged and traceable. This was important for compliance and for understanding how the agents were performing.

Launch

The app features authentication, dashboard views, lead management, and a solid foundation for AI agents. While the AI agents continue to be refined, the core CRM functionality provides immediate value, and the automation layer is ready to scale.