TRAILS.TEAM AI: Turn customer questions into AI-ready paths and actions

I built TRAILS out of frustration with two things: the growing amount of AI slop flooding the internet and the repetitive work involved in SEO and GEO optimization. As someone who works with websites every day, I wanted to automate the tedious parts of my SEO and GEO workflow without sacrificing content quality. But I also noticed something else: People increasingly ask AI assistants questions that businesses could easily answer, yet their websites often don't provide that information. Instead of finding reliable answers, AI systems sometimes make assumptions, hallucinate, or simply recommend someone else. I wanted to change that. Rather than generating endless generic articles, TRAILS identifies the questions customers are actually asking and helps businesses turn those unanswered questions into useful, accurate information. But my vision goes beyond making websites easier for AI to understand. I started wondering: Why should AI assistants only read websites when they could actually interact with them? Why shouldn't someone be able to ask an AI assistant to find a service, check availability, make a booking, or send an inquiry directly through a company's website? That's where MCP (Model Context Protocol) comes in. I wanted to make websites not just discoverable by AI, but accessible and actionable for AI agents, without requiring businesses to build complex integrations themselves. TRAILS combines these ideas: automating meaningful SEO and GEO optimization, following the paths customers take through their questions, and connecting websites directly with AI agents. Less AI slop. More real answers. Websites that don't just inform AI, but work with it.