Finding Paying Customers Abroad Starts with Google Suggest, Not an AI Chatbot
Shipping an AI-powered tool is cheap; shipping one nobody searches for is the real cost. This workflow replaces speculative prompting with demand signals pulled directly from search behavior, giving solo builders and small teams a repeatable way to validate before they build.
Brainstorming with an LLM tends to produce plausible-sounding but commercially hollow ideas — tools nobody searches for or that already have mature competitors. A more reliable path starts with Google Suggest: feed a root keyword into the search bar, cycle through a–z prefixes and suffixes, and harvest the autocomplete suggestions. Those are real queries from real users.
Root keywords aren't dictionary words; they're user actions — generate, calculate, convert — paired with output formats like PDF, image, text, or video. That combinatorial matrix produces a first batch of candidate keywords. Each candidate then gets expanded through Google Suggest again, and high-ranking tool sites are mined for their own SEO keyword clusters, creating a self-reinforcing loop of word-to-site-to-word discovery.
The whole process can be automated. Google Trends data comes in via RSS, Google Suggest has a callable API, and an agent like Hermes can be configured to crawl daily, filter viable tool-site keywords, and deliver a curated list every morning.
LLMs are poor at demand discovery because they lack access to live search-volume data and competitive landscape signals; they generate ideas that sound right but carry no market evidence.
The combinatorial matrix of action verbs × output formats is a lightweight but effective substitute for formal keyword research when you have no budget for tools like Ahrefs or Semrush.
Automating the workflow with an agent closes the gap between a one-off brainstorming session and a continuous demand-sensing pipeline, which is closer to how professional SEO teams operate.