Entrepreneurs and small business owners worldwide face a persistent information deficit when evaluating the financial and market viability of physical retail locations. Traditional market research remains prohibitively expensive, largely qualitative, and depreciates rapidly as economic conditions shift. This paper presents Locon, a full-stack, AI-driven geospatial market analysis platform designed to systematically address these challenges through engineering. Locon integrates real-time web scraping of global cost-of-living indices from Numbeo, spatial competitor detection via the Google Places Nearby Search API, and deterministic financial modelling rooted in Purchasing Power Parity (PPI) heuristics to produce a comprehensive viability assessment for any selected geographic coordinate. The Haversine formula is applied on the backend to enforce strict metric-radius competitor filtering, replacing the imprecise bounding-box approximations common in consumer mapping applications. A deterministic 0–100 Success Score is computed by combining competitor density penalties, capital sufficiency bonuses, and existing competitor reputation scores. Finally, a Generative AI assistant powered by Google's Gemini 2.5 Flash model contextualises the quantitative output into actionable advisory narratives. Empirical testing across multiple city configurations—including           Mumbai, London, and New York—demonstrates capital projections conforming within ±12% of verified commercial leasing benchmarks. The platform is deployed in a fully decoupled architecture with a React 19 + Vite frontend hosted on Netlify and a Python FastAPI backend on Render, backed by a SQLAlchemy ORM layer supporting both PostgreSQL and SQLite.