RAG systems for Real Estate.
Production-grade RAG engineered for real estate and proptech.
SmartDuke builds rag systems for real estate and proptech — systems that ground AI answers in your own data, with citations and freshness guarantees. Real estate is data-rich and tooling-poor. The platforms that ship genuine AI leverage — not just chatbot wrappers — are taking outsized share. Our approach: Retrieval evals before any LLM call, citation-rate monitored as a first-class metric, and explicit refusal when confidence falls below threshold.
The problems
we keep solving.
Real estate is data-rich and tooling-poor. The platforms that ship genuine AI leverage — not just chatbot wrappers — are taking outsized share.
Property research takes hours per deal
Comparables, lot history, zoning, and market analysis require pulling data from a dozen sources for every property of interest.
Document-heavy transactions are slow
Disclosures, contracts, and financing documents are dense and easy to miss critical details in.
Buyer demand for 24/7 availability outpaces agent capacity
Modern buyers expect instant responses; agents have finite hours and lose deals to the first responder.
Three things we'd build
first.
Concrete starting points for rag systems in real estate and proptech. We pick the one with the highest leverage and the cleanest measurement story.
- Idea 01
Property-research agent that pulls comparables, lot history, and market analysis in seconds
- Idea 02
Offer-analysis copilot that highlights non-standard clauses, risks, and negotiation levers
- Idea 03
Document-summary RAG that turns disclosure packets into actionable bullet lists
Production-grade,
from day one.
Hybrid retrieval (BM25 + semantic + reranker), source-priority ranking, freshness gating, and answer-capsule generation with sentence-level citations.
Evals before launch.
Every loop, tool call, and structured output is graded with a frozen test set and an explicit rubric. Failed evals block the deploy.
Telemetry from day one.
Traces, latency budgets, token costs, and error rates wired up before the first user touches the system.
Guardrails as architecture.
Input validation, output verification, escape hatches, and human handoff paths designed in — not bolted on after incidents.
Boring stack on the edges.
Cutting-edge model in the middle. Reliable infrastructure around it. Stability where it earns its place.
- ×Hallucination on long-tail queries
- ×Stale documents silently used
- ×Missing or wrong citations
- ×Retrieval that fails on the queries that matter most
Common questions.
01How long does it take to build rag systems for Real Estate?
How long does it take to build rag systems for Real Estate?
Discovery is one week. A working prototype (Spark) is 2–3 weeks. Full production Build for real estate and proptech typically runs 8–12 weeks depending on data complexity, integrations, and compliance scope. We commit to a precise timeline at quote stage.
02What does pricing for rag systems typically look like?
What does pricing for rag systems typically look like?
Every engagement is scoped to outcomes, not hours. Discovery starts in the low four figures. Spark and Build are priced per project. Embed retainers are monthly. We return a quote within 24 hours of inquiry.
03Can you take over an existing RAG project that's stalled?
Can you take over an existing RAG project that's stalled?
Yes — it's a common engagement. We review what's there, tell you honestly what stays and what we'd rebuild, then ship it to production. Real Estate engagements often start this way.
04What's different about your approach to rag systems?
What's different about your approach to rag systems?
Retrieval evals before any LLM call, citation-rate monitored as a first-class metric, and explicit refusal when confidence falls below threshold. We hold the same engineering bar across every engagement, regardless of industry — but the specifics for real estate and proptech are tuned to your trends and pain points.
Same capability, different industry.
Same industry, different capability.
Have an AI product
that needs to ship?
Tell us where you are — early concept, broken prototype, or scaling something that already works. We'll come back within 24 hours with a take and a quote.