Geospatial RAG: connecting AI answers to real places

Two places can have the same name and very different conditions. A document can mention a district without describing the site inside it. A nearby hospital can appear accessible on a map while a railway or river makes the walking route much longer.

These distinctions are central to Geospatial RAG. The task is to retrieve evidence that fits a place and a decision, then combine it with spatial computation. LGM applies this approach to territorial research across geographic layers, catalog information and supporting context.

Why location changes retrieval

A text search for “schools in the northern district” might retrieve planning documents, news and facility descriptions. A spatial investigation needs to establish the district boundary, locate schools and determine whether the records actually cover the study area.

It also needs to account for scale. A national average may provide context but cannot explain conditions on one street. A detailed local layer may support a neighborhood decision while covering only a small part of the city.

Geospatial retrieval therefore asks several questions together: where does the evidence apply, what does it measure, when was it collected and how does it relate to other inputs?

What LGM brings into the context

LGM combines geospatial data access with tools that operate on geometry. Relevant context may include dataset metadata, objects selected within a boundary, raster values, spatial aggregates and explanations of how the outputs were produced.

For a site-selection question, the retrieved evidence could include population indicators, existing competitors, transport access and land-use information. The right selection depends on the decision. A small neighborhood market and a regional distribution center should not be ranked using identical criteria.

Private collections can add context unavailable in public sources, such as the locations of an operator’s facilities or a customer’s development plans.

Retrieval and calculation have different jobs

Retrieval identifies evidence. Spatial tools calculate relationships within that evidence.

If an agent needs the number of buildings inside a polygon, a geographic operation should perform the selection. If it needs an average terrain value, the raster service should compute an appropriate statistic. Language generation then explains the result and its limitations.

This division makes it easier to inspect mistakes. An incorrect boundary, incomplete layer or unsuitable aggregation can be examined as a specific step instead of being hidden inside a fluent paragraph.

What a grounded answer should contain

An answer becomes more useful when its evidence travels with it. For territorial research, that means identifying:

• the study area and relevant time period;
• the datasets and operations used;
• the resulting indicators or geometries;
• important assumptions and gaps in coverage;
• the next checks needed before action.

LGM’s intended research workflow connects these elements through retrieval, computation and verification. A complete record of execution and resource use is a further part of the platform’s development work.

A practical example: access to sports facilities

Suppose a municipality wants to identify neighborhoods that may need more sports infrastructure. The first pass can combine population and known facility locations. It can then compare coverage across areas and return candidate zones.

That output remains sensitive to missing facilities, operating hours, capacity and real pedestrian routes. A useful agent should surface those uncertainties and request additional evidence where necessary. A blank part of a dataset is not automatically an underserved neighborhood.

Explore Geospatial RAG, Tools & Skills to see how these capabilities fit together, or read the Tbilisi case study for a territorial research example.