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Case study

GIS Itinerary Optimization with an LLM Interface

Sub-2-second performance for 10-node itineraries

Full-StackAI/MLGIS & Remote SensingJan–Apr 2026Kishore N and Sathish

The problem

Itinerary planning becomes slow and brittle when route constraints, spatial data, and natural-language requests live in separate tools.

Our approach

We combined spatial routing with an LLM-powered interface so users could describe constraints while the underlying system handled geographic computation.

  1. Ingest and structure multi-source spatial data with GeoPandas and PostGIS.
  2. Generate routes with NetworkX-based spatial optimization.
  3. Connect a ReactJS and FastAPI application layer.
  4. Use the OpenAI API to translate natural-language constraints into route inputs.
  5. Measure performance on 10-node itineraries.
originstop 01stop 02destination
Illustrative route schematic — not a real client route or map dataset.

Technology

GeoPandasNetworkXPostGISReactJSFastAPIOpenAI API

Results

  • Sub-2-second performance for 10-node itineraries.
  • Natural-language constraint handling through an OpenAI API chatbot.

What this means for your project

This is the pattern behind a route and spatial optimization system: spatial computation remains explicit while the interface becomes easier for people to use. Explore the matching service

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