Most people use coding agents like Claude Code to build toy apps and glorified demos. In this video, I show you what it looks like to go from prompt to an actual sellable product in one session—starting with the business angle, not the tech. We build an AI brand monitoring app where you can pick a brand, write the prompts you want to test, choose providers (ChatGPT, Perplexity, Gemini, Grok, Copilot, plus Google Search), and then run scans to see if the brand is mentioned at all—and even infer sentiment from the responses. The key idea is this: for brand monitoring, calling LLM APIs isn’t enough. I want the exact user-facing responses people see inside the real web apps (and in Google’s AI mode), because those results differ from APIs and can be geo-dependent. That’s why I use Bright Data’s AI web scrapers and the production-ready trigger → poll → download pattern (snapshot IDs, progress checks, retries). I also walk through “context engineering”: feeding the agent docs, example payloads, and error messages so it doesn’t guess. We implement the Run Scan workflow in a Next.js + SQLite + Drizzle setup, debug provider-specific quirks (like Copilot’s payload), and set the stage for making it robust with retries, concurrency limits, and background jobs using Inngest.