Enterprises, retailers, and logistics operators shouldn't have to write complex GIS scripts or GIS database queries to understand locations. MapaBharat combines Galum — our sovereign natural-language geospatial AI agent — with OpenStreetMap primitives, high-speed routing, and multi-polygon spatial operators to answer high-stakes commercial questions in plain English and native Indian languages.
Explore real query sessions captured from MapaBharat on mobile devices. Observe how Galum converts vague business intentions, non-English phrasing, and topological constraints into precise map features and clear rationales.
Traditional map APIs require hardcoded category IDs and radial latitude/longitude queries. MapaBharat understands the underlying economic geography and linguistic nuances of Indian cities.
Search for business opportunities rather than mere names. Galum infers the surrounding demand factors — identifying footfall generators, transit intersections, competitors, and complementary commercial establishments.
Execute complex arterial queries like "amenities along highway routes within 1 km". MapaBharat combines OSRM road corridor tracing with exact vector buffer sampling to extract roadside facilities.
Supports native prompts in Indian languages (Bengali, Hindi, Tamil, Telugu, Marathi, and more). Returns plain-language rationales and localized explanations without requiring translation pre-processing.
Resolves complex polygonal boundaries including parks, university campuses, lakes, industrial estates, and residential complexes rather than collapsing locations into naive single coordinates.
Built on OpenStreetMap and self-hostable spatial infrastructure. Eliminates proprietary vendor lock-in, recurring per-hit map API fees, and restrictive commercial licensing constraints.
Every search result comes with an explicit explanation of why the area was chosen, which tags were extracted, and what spatial boundaries were enforced — ensuring auditability for enterprise teams.
Screenshots and keyframes captured directly from real MapaBharat production sessions. Observe how Galum interacts with the user, displays live reasoning spinners, and visualizes multi-layer geospatial data.
When a user asks a complex natural query (e.g. "Find open spaces within 1 km of the road"), Galum acknowledges the request asynchronously with a status indicator (Galum is getting data...) while the background execution pipeline parses the spatial geometry.
Galum traces the arterial road (orange LineString corridor between Darjeeling, Ghoom, and Sonada) and applies an exact 1 km geometric buffer boundary (blue outline), extracting enclosed meadows, open spaces, and natural zones along NH110.
Users can pan and zoom directly into specific sectors of the corridor (e.g., around Glenary's and Snowview Hotel). The underlying vector tiles seamlessly render topological boundaries, roads, and reserve forest lines without latency.
When searching for "Open spaces in Lokhandwala Complex", Galum avoids imprecise point coordinates and instead isolates exact parcel geometries — highlighting parks, playgrounds, and recreational grounds across Andheri West.
Selecting any polygon or pin opens an instant popup with verified OSM entity data (e.g. Skywalker (Atlas-Skywalker) housing enclave), allowing real estate analysts and logistics dispatchers to verify exact site coordinates.
Galum natively understands queries written in Bengali, Hindi, Tamil, and other Indian scripts. It traces the route from Maheshtala to Gariahat in Kolkata, filters matching coffee shops, and delivers the rationale in fluent Bengali.
A four-tier pipeline connecting natural-language user semantics with high-speed GIS geometry engines and deterministic spatial databases.
Ingests natural language in any Indian language or English along with client viewport coordinates (bbox) and conversational session context.
Galum evaluates commercial intent, maps semantic tokens to OSM keys/values, chooses spatial geometry modes (Point, Route, Polygon), and drafts the rationale.
Compiles Overpass QL expressions, resolves bounding areas via Nominatim geocoding, and performs corridor route sampling via OSRM vector buffering.
Renders rich GeoJSON layers on high-performance vector maps (Mapbox GL JS) or streams structured results over asynchronous polling REST APIs.
Designed for retail chains, delivery aggregators, urban planning consultants, and logistics enterprises that require deep spatial insight without proprietary lock-in.
Commercial expansion teams can analyze footfall magnets, existing competitors, public transit corridors, and college campuses within minutes. Instead of manual surveys or rigid GIS queries, planners simply ask:
Fleet operators, cold-chain distributors, and infrastructure developers can evaluate facilities situated along transit arteries and national highways:
