MLS, CRM and public records in one spatial engine.
Tell us your sources and workflow. AI-native engineers integrate MLS, CRM and public records into a PostGIS spatial engine, run the analysis that scores opportunity, and serve it as a live map wired into the tools your users already run.
Start with where the data actually lives
MLS, CRM and public records never share a schema.
Three sources, no common model
MLS, your CRM, and public parcel, zoning and permit records — none of which agree on how to describe the same property.
So the answer comes out by hand
Your platform cannot answer “where is the best opportunity” without someone assembling it manually, every time.
And it chokes on concurrency
A platform built by an agency with no geospatial depth crawls the moment more than a handful of brokers open the map at once.
Which means the platform never does the one job it exists to do: surface the right opportunity — fast enough, and for enough people at once, to be worth opening twice.
So here is the engine underneath
Map + Analysis, Wired Into Your Flow
Integrate the sources, normalize them into one spatial model, run the analysis that scores opportunity, and put the answer back where your users already work.
Integrate and normalize
MLS through the RESO Web API, your CRM, and public parcel, zoning and permit records — normalized into one PostGIS model.
Analyse it, then score the opportunity
Spatial and statistical analysis — proximity, suitability, zoning fit, comparables, trend — modelled rather than hand-weighted, so the platform answers “where” instead of listing everything and leaving the judgement to the user.
Serve it, and wire it back
A live Mapbox GL map that clusters cleanly at thousands of listings, with webhooks back into your CRM and sales flow.
The map is what your users see. The analysis underneath is what makes them open it a second time — and it is where being AI-native actually shows: the scoring is ML-driven wherever that beats a static rule, and the data analysis is a first-class part of the platform rather than an afterthought. The concurrency work is what keeps it usable when the whole brokerage is on it at once.
So here is one we built
FuseGIS: the records that signal where to invest.
We built FuseGIS, a real estate intelligence platform that ingests and maps zoning, parcel and permit data across Houston and Austin — the exact records that signal where to invest — normalized in PostGIS and served through Mapbox on AWS, built to scale. Data analysis and live mapping from one experienced, AI-native nearshore team, one to four hours off US time.
Analysed, not just plotted
Proximity, suitability, zoning fit and comparables scored in PostGIS — so the map answers a question instead of posing one.
Wired into the flow
Webhooks back into your CRM and sales process, so what the map surfaces actually reaches the person who acts on it.
Tech stack
- RESO Web API
- PostGIS
- Mapbox GL
- Data analysis
- Python
- AWS
Score opportunity, don't just map it.
MLS, CRM and public records fused into one spatial engine, analysed, mapped live and wired into the workflows your users already run — a platform that surfaces the right opportunity and holds up under load.
So bring us your sources.
Which MLS feed, which CRM, which public records, and what your users are trying to decide — straight to the engineers who would build the engine. We'll tell you what the spatial model looks like and where concurrency bites first.
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