AI infrastructure for real estate

A real estate deal is a small mountain of documents and a long thread of context. Contracts, inspection reports, disclosures, and listings on one side; buyer preferences, showing history, and the back-and-forth of a negotiation on the other. Both tend to be scattered, and a tool that can't pull them together leaves the agent reconstructing where a deal stands from memory and email. If you're building deal management or property tools, the infrastructure you need underneath handles the documents and the deal context, kept isolated per transaction.

This page is for teams building real estate tools. Exabase gives you document processing through Extract, per-deal context through Memory, and per-transaction isolation through Bases. Each deal gets its own environment holding its documents and its history, so an agent works from the full picture of a transaction rather than piecing it back together.

What you can build

Real estate tools tend to be one of a few shapes, each on infrastructure that already exists.

A deal management tool that holds everything about a transaction, documents, buyer preferences, showing history, the state of the negotiation, isolated per deal, per-transaction Bases with memory for the deal thread.

A property document processor that extracts structured data from contracts, inspection reports, and listings, the document extraction at scale pattern applied to property paperwork.

A buyer-matching assistant that remembers a buyer's preferences and showing history and surfaces properties and context accordingly, memory of buyer context.

A searchable property and document archive where past deals, contracts, and reports stay findable by meaning, Deep Search over stored documents.

Real estate problems, solved

The problems real estate builders run into are specific, and each has an answer.

Documents in every format. Contracts, inspection reports, disclosures, and listings arrive as PDFs and scans. Extract turns them into clean, structured, searchable text through one API, including scans, so the paperwork becomes usable data rather than files in a folder.

Deal context scattered across the process. A transaction unfolds over weeks, and the context, what the buyer wants, what was shown, what was offered, accumulates in pieces. Memory holds the deal thread and keeps it current through contradiction resolution, so an agent picks up where the deal stands rather than reconstructing it.

Keeping deals isolated. Each transaction's documents and context should be its own. Bases make isolation structural, one per deal, from a single API call, so one transaction's context doesn't bleed into another and each deal is searchable on its own. It's the multi-tenant memory pattern, applied per transaction rather than per customer.

Buyer preferences across showings. A buyer's tastes emerge over a series of viewings. Memory holds preferences and showing history, so a matching agent works from an accumulating picture rather than the last conversation.

The infrastructure underneath

Four primitives carry most real estate tools. Extract turns contracts, reports, and listings into structured data. Memory holds buyer preferences and deal context across the transaction. Bases isolate per deal from a single API call. Deep Search makes documents and past deals findable by meaning. One API key, rather than assembling extraction, memory, and isolation yourself.

Deal context that stays whole

A real estate tool on this foundation keeps a transaction's context intact in a way scattered files and email threads can't. Each deal's documents and history accumulate in its own environment as the transaction progresses, so an agent always has the full picture, and because the memory self-organises, that picture stays current as terms change rather than going stale. Per-deal isolation scales from one API call, so a brokerage can run thousands of transactions without the separation getting fragile, and the searchable archive of past deals compounds into an asset, paid for once at extraction and useful for reference and matching long after a deal closes. Buyer context carries across showings and even across transactions, so the tool gets better at matching the longer it knows a buyer.

Get started

Start with the getting started guide, then the use-case pages that match what you're building: document extraction at scale for property documents, long-term memory for any agent for deal and buyer context, and multi-tenant memory for SaaS for per-deal isolation. There's a free tier to build against.

FAQs

Can it extract data from contracts, inspection reports, and listings?

Yes. Extract turns property documents into clean, structured, searchable text through one API, handling scans as well as native PDFs, so the paperwork becomes usable data.

How is each deal's context kept separate?

Each transaction gets its own Base, a structurally isolated environment, so one deal's documents and context don't bleed into another's. It's the multi-tenant SaaS pattern applied per transaction.

Does it track buyer preferences and showing history?

Yes. Memory holds a buyer's preferences and showing history across the process and keeps it current through contradiction resolution, so a matching agent works from an accumulating picture rather than the last conversation.

Does an agent know where a deal stands across the process?

Yes. Memory holds the deal thread, what was shown, offered, and agreed, and keeps it current, so the agent picks up the current state of a transaction rather than reconstructing it from email.

Can past deals be searched later?

Yes. Stored as searchable content and indexed for Deep Search, past deals, contracts, and reports stay findable by meaning, useful for reference and matching.

Is this a finished real estate product or something I build on?

Something you build on. Exabase is the infrastructure, document extraction, deal and buyer memory, and per-deal isolation, and you build the deal management or property tool on top.