Location intelligence without the metered bill.
Analytics platforms embed maps and immediately meet two problems: per-load pricing and data egress. Self-hosted MapMap solves both, with geocoding, travel-time matrices and isochrones as plain APIs your pipeline and your analysts' AI agents can call all day.
The location layer as infrastructure, not a line item.
Every block below is a shipped API already driving the public analysis demos on this site.
Addresses in, coordinates out
Forward and reverse geocoding turn messy address columns into coordinates and back, with structured results your pipeline can trust and interrogate. Batch it on your own box without a rate card watching.
Travel time is the honest distance
Matrices score datasets by real travel time instead of crow-flies kilometres: territory design, catchment overlap, service equity. This grid is one real matrix call, four Greater Manchester towns against four destinations, minutes exactly as returned.
Isochrones for real catchments
Drive, cycle and walk-time polygons on the actual road network, ready to intersect with population or customer data. This is our public EV-gap study doing exactly that analysis, live.
See the study live →
Straight into your GIS stack
OD matrices export to CSV shaped for kepler.gl, and every endpoint speaks plain GeoJSON, so results drop into the tools your analysts already run rather than a proprietary viewer.
OD matrix guide →Your data never leaves the pipeline
Customer datasets should not transit a map vendor to get enriched. Docker distribution inside your own network: no egress, no per-call metering, an SBOM and security questionnaire for the review.
Your analysts' agents can call all of it
The hosted MCP server exposes geocoding, matrices, isochrones and local geometry tools to AI agents directly, so the analysis copilot in your notebook can compute with the engine instead of estimating from training data.
Known limit: there is no administrative-boundary dataset or footfall data product; bring your own polygons and demographic data, as our public studies do with ONS and open registries.
We will build your location analysis as a working demo.
Give us the dataset and the question, and our AI agents will build the analysis on MapMap: geocoded, matrixed, mapped and exported to your GIS stack. The public coverage studies on this site were built exactly this way.
Automotive
MapMap in the vehicle: a full navigation stack you host and control.
Trucking & Logistics
The router that refuses the bridge before your driver ever sees it, with dispatch-grade optimisation built in.
Bus & Fleet Operations
Every vehicle, on one live map, running on infrastructure you host and control.