Real Estate & PropTech AI Engineering

Accuracy depends on data density and market volatility, so we measure on your comparables rather than quoting a figure. The more important property is explainability: a valuation must show its drivers to be defensible. A confident number no one can explain is a liability, especially where fair-housing scrutiny applies.

Yes, if unmanaged, because features like location can proxy for protected characteristics. We test for that and expose the drivers, so a valuation can be defended. Fair-housing risk is the reason we treat explainability and bias testing as core to the build, not optional extras added at the end.

A large share of lease abstraction and closing paperwork, with straight-through handling on routine documents and a human check on high-value ones. The value is in freeing your team from repetitive reading. We design the exception path carefully, because that is where a lazy automation quietly makes costly mistakes.

Yes. We integrate with your listing, transaction, and CRM systems through their APIs rather than replacing them. Most firms run tools that work but hold data in silos, so the practical path is to connect them cleanly and let models read and write where it is safe.

You do. Your listing, transaction, and document data, the trained models, and the code remain yours, in your environment. We build on your stack and hand over documentation, so there is no lock-in to us in what we deliver.

There is no single price; cost tracks scope. A single document automation build is a modest, weeks-long project, while a wider rollout across your listing, transaction, and document systems is larger. We scope from one use case, quote a fixed range up front, and sequence so early value funds the next step rather than pricing everything at once.

The return comes from faster closings, defensible valuations, and better-qualified leads, set against build and running cost. It only holds when the model targets a real, measured cost, so we baseline first and report value against it. We would rather size the return honestly on your numbers than quote an industry average that may not fit you.

A focused pilot on one use case such as document automation usually reaches a working version in a few weeks, then tuning on real data. Wider rollout takes longer. We start narrow, prove the numbers, and extend, so you see value early instead of waiting months for one large launch.

Common ones are automated valuation, lease and document automation, lead scoring, and portfolio analytics. The best first project is the one tied to your biggest measurable cost or opportunity, not the most advanced-sounding option. We help you pick the use case where value is fast and the data already supports it.

We start with a short feasibility check on one use case: does the data exist, is it usable, and does it hold the signal the model needs. Often you already have more usable data than you expect. We assess it before recommending any build, so the first step is a decision, not a commitment.