Can you source engineers with RESO Web API or RETS experience?

We have engineers who've worked with MLS integrations: RETS feed processing, RESO Web API, and the data normalization work that comes with it. This is a niche but real capability in our network.

What about geospatial engineering with PostGIS or Elasticsearch?

Geospatial experience is something we specifically ask about for proptech roles. Engineers with Mapbox, PostGIS, and Elasticsearch geo queries are in our network.

Do you work with PropTech startups or only established companies?

Both. Our startup solution works well for early-stage proptech companies that need to move fast. Larger platforms often use our staff augmentation model to fill specific capability gaps.

Real Estate & PropTech

Proptech moves slow until it doesn't.

Listing platforms, transaction management, CRM integrations, and map-based search: built by engineers who understand that real estate data is messy and agent workflows are opinionated.

Build Your Team How It Works

What makes proptech hard to build

Real estate data is fragmented by design.

Data is fragmented across MLSs

There are hundreds of MLS boards in the US, each with their own data feed format, update frequency, and licensing terms. Aggregating listing data reliably is a real engineering problem.

Property data is inherently hierarchical

State, county, city, neighborhood, building, unit: each level has its own data sources, inconsistencies, and update cadence. Search that works across all of it requires careful data modeling.

Transaction workflows are complex

Offers, counteroffers, contingencies, escrow, title, and closing: each state has different rules and timelines. Software that manages transactions needs to handle this without hiding it.

Map performance at scale is hard

A map with 100,000 property pins that stays responsive under user interaction requires real work: clustering, viewport-based loading, and careful tile strategy.

What we build

The real work proptech teams ask us for.

5 use cases

Listing search and discovery

Elasticsearch-based property search with geospatial queries, faceted filters, and map-based browsing. Engineers who've dealt with MLS data normalization.

Map interfaces and geospatial features

Mapbox and Google Maps integrations, polygon-based search, drive-time isochrones, and school district overlays. Real geospatial engineering, not just dropping pins on a map.

MLS and IDX integrations

RETS, RESO Web API, and vendor-specific data feeds. Engineers who've dealt with the inconsistencies in MLS data and built reliable sync pipelines.

Transaction and document management

Digital signature workflows, document storage, offer management systems, and timeline tracking for real estate transactions.

AVM and market analytics

Automated valuation models, comparable sales analysis, market trend dashboards, and price prediction features.

AI in this industry

Automated valuation models, AI-powered lead scoring, and generative listing descriptions are becoming standard proptech features. The engineering challenge isn't the model: it's the data quality. MLS data inconsistencies, address normalization, and property record matching are what actually determine whether an AVM is useful or embarrassing.

Common tech stack