Market Mage

This IS NOT a prompt.

Market Mage is a multi-file market research system that gathers and organizes available evidence into actionable intelligence for any city, state, ZIP code, county, service, job, vertical, or niche you point it toward.

the demo clip shows a run of 2 minutes to find the information on a potential tote rental business in Chicago.

GPT 5.6 SOL: "For that Chicago tote-rental audit, a competent person would likely spend: Initial research: 6–12 hours Finding competitors, pricing, local demand signals, customer pain, market context, and acquisition clues.

Verification and source cleanup: 2–4 hours Opening sources, checking that pricing and claims are current, removing weak evidence, and recording links.

Report construction: 2–4 hours Organizing the scorecard, competitor table, verdict, offer direction, risks, and roadmap.

The realistic human equivalent is roughly 10–20 hours for a solid version. A more polished consultant-grade report could easily take 20–30 hours. The demo is therefore showing acceleration of roughly 50–150×"

It investigates market viability, customer pain, competitors, pricing, positioning, acquisition opportunities, and next-step execution, then turns the findings into a structured decision ready audit.

Validating a new business idea or niche can mean spending hours digging through scattered sources like Reddit, LinkedIn, and ad libraries. Turning raw market data into a clear offer and execution plan is often slow, messy, and vulnerable to confirmation bias. This tool scores niche viability by looking at factors like market size, pain level, and buying power, while automatically breaking down competitor funnels, ads, and pricing. It then creates a detailed offer blueprint with unique hooks, risk reversals, and pricing, plus a multi-channel acquisition plan that spans cold email, paid ads, and content strategy. Finally, it lays out an 8-week execution roadmap with clear growth milestones. Powered by SeleniumBase, Scrapling, yfinance, OpenBB, and OSINT tools, it follows an evidence-first process that beats generic LLM prompts by grounding strategy in real market signals. It’s great for validating service niches, spotting competitor gaps, building business cases, and crafting high-conviction offers—though deep dives may take longer if public data is sparse.