A team that owns your geospatial pipeline, raw data to live map.
Tell us where your data comes from. AI-native engineers build the pipeline (ingestion, geocoding, PostGIS analysis, Mapbox GL serving) and do the data analysis on top, automated and ready to scale.
Start with what a map demo doesn't show you
Any team can render a map with a little clean test data.
Six sources, six shapes
Real data arrives inconsistent — and one source quietly changing its shape takes the whole map down with it.
Geocoding quality varies
The same address resolves differently across sources, so one place lands in three spots and nobody trusts the layer.
Volume turns queries into full-table scans
What was instant on test data crawls the moment production volume arrives, and no amount of front-end work fixes it.
The tell is a partner who says “we do maps” with no named pipeline behind it — no PostGIS strategy, no tiling, no ingestion layer. The map is the surface. The pipeline underneath is the product.
So here is every stage we own
The Whole Pipeline, Automated
Connectors, normalization, geocoding, data analysis and serving — automated end to end by AI-native engineers, with ingestion that self-heals when a source changes shape.
Ingest and normalize
Connectors fetch from each source; normalization and dedup turn them into one dataset. When a source changes shape, ingestion absorbs it instead of paging someone.
Geocode, then actually analyse it
Geocoding first, then the data analysis that turns rows into an answer — spatial queries in PostGIS against partitioned tables, plus clustering, scoring and anomaly detection. Fast as the table grows, not just while it is small.
Serve it as tiles
Mapbox GL vector tiles or OGC endpoints, built to hold at production volume rather than at demo volume.
None of that is visible in a map demo, which is exactly why it is the part worth asking about. And we don't just move the data — we analyse it: spatial and statistical analysis, with ML where it earns its place, from AI-native engineers who build with agentic tooling. Ask any partner to name their ingestion layer, their PostGIS strategy and their tiling approach. The answer tells you what you are buying.
So here is one we built
FuseGIS: zoning, parcel and permit data across two metros.
We built FuseGIS, a real estate intelligence platform that scrapes, normalizes, dedupes and maps zoning, parcel and permit data across Houston and Austin on a PostGIS/Mapbox stack on AWS. Multi-source ingestion, automated cleanup, served through a live console — the whole pipeline, not the map on top of it.
Self-healing ingestion
Sources change shape without warning. The connectors absorb it instead of taking the map down at 3am.
Analysis, not just storage
Clustering, scoring and anomaly detection on the data once it lands — so the pipeline answers questions rather than only holding rows.
Tech stack
- Mapbox GL
- PostGIS
- QGIS
- Geoserver
- GeoPandas
- Python
- Google APIs
- OSM
Real geoprocessing, not a map widget.
A geospatial pipeline that holds at production volume — multi-source ingestion, geocoding, data analysis and vector-tile serving — architected end to end by AI-native engineers, not a Mapbox layer glued onto a fragile backend.
So bring us your sources.
Where the data comes from, how often it changes, and what you need the data to tell you — straight to the engineers who would build the pipeline. We'll tell you what breaks first at your volume, and what it takes to keep it standing.
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