Aaloka MCP
Geospatial analysis at the speed of a prompt.
Aaloka's location intelligence, inside the LLM you already work in. Ask in plain language — get back real geometry and maps you can use.
ClaudeChatGPTCursorAny MCP client
In your LLM
> Map 5 km catchment around our 14 Bengaluru outlets and shade by affluence.
aaloka.catchment(radius_km=5, from="outlets/blr")
aaloka.enrich(layer="income_hh", vintage="2026Q2")
aaloka.render(kind="choropleth")
→ 14 polygons · 2.1M households · GeoJSON + PNG returned
Request a beta invite
Invites go out in waves.
What it does
Six primitives. Everything else is a prompt.
Catchments
Radius or drivable-distance boundaries — returned as GeoJSON, not a picture of one.
Demographic enrichment
Population, income, age and consumption bands joined to any geometry, at grid or ward resolution.
Render-ready visualizations
Choropleths, heat maps, point clusters and points — as SVG or map tiles you can re-theme.
Bring your own layers
Push your outlets or customer lists into a session and analyze them against Aaloka's base data.
Any MCP client
One config line. If it speaks MCP, it speaks Aaloka — no SDK, nothing to rewrite.
Traceable answers
Every response carries its source, vintage and method — defensible line by line.
Built on LatLong's data
3.3M km²
Coverage across India, gridded to 100 m
660k+
Villages, plus 5k+ urban local bodies
4×
Accuracy against the next-best available base
30M+
Points of interest, classified and geocoded
120+
Datasets, from census to competition
Update frequency varies by dataset, from monthly to quarterly.
The private beta opens in September 2026.
A small first cohort. Existing Aaloka customers go to the front of the queue.
