Coverage
The real world, mapped beyond listings.
Evendo’s LocationGraph builds a place-first model of physical reality: broad enough for global discovery, deep enough for AI systems to reason about intent, context, and execution.
2M+places already mapped
150+semantic attributes
65experiential vectors
Places
Signals
Vectors
Context
Execution
LocationGraphcoverage + depth
Coverage is not inventory
Most platforms map what can be sold. Evendo maps what exists.
Coverage is created independently of supplier participation, digital integrations, or immediate monetisation — so the graph can represent reality, not just bookable supply.
Inventory-shaped maps.
- Coverage depends on supplier opt-in.
- Places are reduced to listings or products.
- Offline environments remain structurally invisible.
- Depth is limited by commercial metadata.
Reality-shaped graph.
- Places exist as first-class semantic entities.
- Coverage is generated proactively.
- Nodes persist whether or not a place is digitally bookable.
- Every node can accumulate context, behaviour, and execution state.
How coverage is created
A graph that expands before the market asks for it.
The LocationGraph is not a catalogue waiting for suppliers to join. It is a continuously expanding model of places, areas, neighbourhoods, venues, landmarks, experiences, and the relationships between them.
Proactive discovery
Locations are identified and mapped independently of supplier registration.
Canonical identity
Multiple references resolve into stable place entities that can persist and improve over time.
Spatial containment
Places inherit and contribute context through cities, districts, neighbourhoods, and micro-areas.
Continuous enrichment
Signals update the graph without flattening historical or behavioural context.
Depth is what makes coverage useful
Every place becomes more than a pin.
Each LocationGraph node can carry spatial, semantic, temporal, experiential, behavioural, and execution context — turning flat place data into AI-readable world state.
Where it sits
Neighbourhood, district, city, region, proximity, containment, walkability, transit context, and surrounding anchors.
What it means
Categories, descriptors, intent surfaces, relevance signals, entity relationships, and canonical graph identity.
How it feels
Atmosphere, audience fit, mood, suitability, style, locality, accessibility, and behavioural expectations.
When it matters
Seasonality, time-of-day relevance, weather fit, opening patterns, event dynamics, and moment-specific usefulness.
How people use it
Discovery behaviour, preference signals, route patterns, conversion feedback, and intent formation across sessions.
What can happen next
Bookability, contactability, coordination requirements, action paths, verification, fulfilment, and outcome state.
Semantic density
The same place can answer thousands of different intents.
A museum is not just a museum. It may be quiet, rainy-day friendly, intellectually dense, good for solo travellers, near public transport, poor for young children, strong for architecture, weak for nightlife, and executable through different paths depending on context.
“museum in Madrid”
A category and a geography. Useful, but shallow.
“quiet rainy-day culture, solo, transit-friendly”
A contextual intent object the graph can reason against.
Coverage engine
Coverage compounds when breadth and depth reinforce each other.
More places create more discovery surface. More discovery generates more intent. More intent improves enrichment. More enrichment makes the graph more useful for AI-native interaction.
Map the real world
Build place coverage independently of supplier participation or integration availability.
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Enrich the nodes
Attach semantic, experiential, spatial, temporal, behavioural, and execution context.
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Expand the surface
Every node creates more ways for AI systems and users to discover, reason, and act.
Why depth matters
APIs cover only a fraction of physical reality.
The LocationGraph is designed for environments where data is fragmented, context is messy, and real-world execution often requires more than a programmable endpoint.
1Coverage includes places whether or not they are digitally bookable.
2Depth captures the context needed to reason about suitability.
3Execution state connects discovery to what can actually happen next.
Coverage territories
Designed for long-tail physical environments.
Evendo’s coverage is strongest where ordinary digital infrastructure becomes thin: fragmented supply, local knowledge, nuanced experiences, offline coordination, and place-based intent.
Urban context
Districts, neighbourhoods, venues, landmarks, transit, culture, nightlife, food, shopping, and local movement patterns.
Intent surfaces
Activities, tours, attractions, events, cultural sites, hidden gems, seasonal contexts, and traveller fit.
Stay context
Hotels, areas, nearby anchors, practical access, atmosphere, audience suitability, and surrounding real-world options.
Offline reality
Restaurants, independent venues, services, informal workflows, and places without modern integration infrastructure.