Non-technical users—knowledge workers, small-business operators, educators and hobbyists—want the productivity gains from LLMs but repeatedly hit friction: confusing model choices, opaque cost/latency tradeoffs, brittle prompt engineering, and fears about data leakage. That gap leaves large addressable demand unrealized: roughly 200 million potential users who could spend an average of $125/year, implying a $25.0B market. You could build a simple, polished chat UI that hides model plumbing while exposing practical controls: curated templates and workflows, per-chat cost estimates and token budgets, one-click connectors to docs and cloud storage, and an optional self-hosted runtime for privacy-conscious users. Delivering web, desktop and mobile clients plus a template marketplace and clear admin controls would make advanced LLM capabilities immediately usable for non-technical audiences. The timing is favorable because high-quality open weights and efficient runtimes are lowering hosting costs and licensing friction, indie SaaS trends support $5–$15 monthly plans, and demand for self-hosting is rising. To stand out against medium competition you must focus ruthlessly on UX, privacy-first defaults, predictable pricing, and curated starter solutions; be honest about the hard parts—model maintenance, latency/cost tradeoffs, and enterprise support—and plan resources for SLAs and ongoing template curation.