LocationGraph turns the physical world into structured, canonical, and machine-readable place intelligence — so AI systems can understand not just where a place is, but what it means, who it suits, when it works, and how it can be acted upon.
Traditional maps are built for human navigation. LocationGraph is built for AI reasoning: canonical places, spatial hierarchy, semantic context, experiential fit, behavioural signals, and execution readiness connected into one infrastructure layer.
Useful for finding a named place or browsing nearby options. But the model is mostly static, category-led, and optimized for human interpretation.
Designed for AI systems that need to reason about suitability, constraints, ambience, context, timing, and operational actionability.
AI systems cannot reliably act on the physical world if every source describes the same place differently. LocationGraph resolves fragmented references into stable entities that can accumulate knowledge, history, and execution feedback over time.
LocationGraph extends place intelligence beyond address, category, and popularity. Each node can represent atmosphere, audience fit, accessibility, transport context, temporal dynamics, style, social relevance, behavioural suitability, and execution feasibility.
LocationGraph allows AI systems to answer travel questions that are not really “searches” at all. They are structured expressions of mood, context, constraints, timing, company, and desired outcome.
Resolved across indoor suitability, ambience, cultural density, transit access, crowd patterns, and neighbourhood logic.
Matched through audience fit, local relevance, temporal load, and place-to-place traversal.
Combines emotional fit, style, proximity, time-of-day behaviour, and execution readiness.
Routes structured demand into Eve and the execution layer when the intent becomes actionable.
Unstructured, contextual, and operationally dependent.
Converted into a structured, location-bound intent object.
Grounded in canonical place nodes instead of flat listings.
LocationGraph is not a static dataset. It compounds through coverage creation, user behaviour, supplier participation, and Eve-driven execution feedback. As more places become represented, discovered, and acted upon, the world model becomes more complete and more difficult to replicate.
LocationGraph is the foundation for AI systems that need to discover, evaluate, explain, and execute against real-world places.
