Real Estate AI Assistant
for Property Intelligence (Under NDA)

An LLM-powered assistant that gives real-time access to structured and unstructured property data, retrieving docs, valuations, and compliance info via natural-language queries to speed responses.
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legaltech
Location
Europe
Industry
Proptech
Partnership
Since 2024
Team size
7 Engineers
How we started

A Swedish real estate group approached us with a recurring problem: despite having an extensive CRM and data warehouse, their agents still spent hours switching between systems to answer simple client questions. The legacy search engine could not interpret free-form questions such as “Show me all 3-bedroom apartments with 2018 energy certificates in Solna”.

The client sought a context-aware assistant that could interpret conversational queries, connect to multiple internal systems, and surface trusted results instantly. They also required compliance with EU privacy standards and Swedish data-handling legislation.

Partners since 2024
Services Delivered
LLM-based retrieval system, conversational UI,
GDPR-compliant data integration
team Composition
AI Architect, 1 Project Manager, 2 ML Engineers, 1 Backend Developer, 1 Frontend Engineer, 1 QA
Technology Stack
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Why They Chose Us

The firm’s key requirement was precision under legal context and full data isolation. We demonstrated previous expertise in building domain-tuned LLM systems with zero data leakage and auditable privacy controls.
Our architectural approach included isolated compute environments, encrypted embeddings, and a reproducible audit trail — all of which aligned with their compliance standards (SOC 2 and ISO 27001).

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Insights

Common Issues

We Identified

01 Fragmented Data Landscape:
Property data, legal docs, and CRM notes lived in four

separate systems with inconsistent schemas.

02 Inefficient Search:
The existing SQL-based search returned literal
matches only, not contextual ones.

03 Latency:
Queries combining multiple filters took >15 s to execute.

04 Regulatory Risk:
Personal data occasionally surfaced in search responses.

Solutions

What We Did

We built a retrieval-augmented assistant with a hybrid data architecture.

1. Data Unification Layer

  • Developed ETL jobs to aggregate property data from CRM,
    municipal records, and legal archives into a normalized PostgreSQL schema
  • Applied entity resolution to unify duplicate listings and create
    a single property identifier.
  • Integrated a legal-domain taxonomy to standardize clause
    types (e.g., indemnity, arbitration, liability).

2. Vector Retrieval Pipeline

  • Created embeddings for textual content
    (descriptions, inspection reports, communications).
  • Combined semantic retrieval (Pinecone) with symbolic
    filters (price, location, energy class) for hybrid queries
  • Implemented chunk ranking and caching
    to ensure <1 s retrieval latency.

3. Conversational Agent Layer

  • Wrapped the retrieval pipeline in a GPT-4-Turbo-powered agent capable of understanding Swedish/English mixed input.
  • Added guardrails for data privacy — personally identifiable information (PII) is masked before leaving the vector store.
  • Integrated with a React-based dashboard that supports voice and text queries.

4. Compliance and Monitoring

  • Deployed under Azure Confidential Compute to encrypt embeddings at rest and in use.
  • Implemented automatic query
    redaction logs for GDPR auditing.
Impact and Results

Impact and Results

01/
Average agent query time reduced from 14 s to <2 s.
02/
Client response latency dropped by 70 %.
03/
Sales conversion rate improved by 12 % across pilot offices.
04/
Data consistency achieved via unified property identifiers.
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