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Microsoft Real Estate and Construction Switzerland

Properstar

Document Processing · Enterprise Knowledge Search · Real-time Analytics

Using Microsoft Azure OpenAI Service and Azure AI Search, Properstar analyzes unstructured real estate data, provides advanced filtering capabilities, and delivers relevant search results.

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Azure AI Search, we now achieve a 98% match rate, which means that almost every suitable property is covered." We pushed the limits of what Azure AI Search...
By combining Azure OpenAI and Azure AI Search, we now achieve a 98% match rate, which means that almost every suitable property is covered." We pushed the...
By next year, it will have a footprint in 65 countries.
Doing so significantly enlarges the data set, as around 40% more information is collected for each property.

Reported impact

Business outcome

"The extensive Azure ecosystem allowed us to access cutting-edge AI tools and slot them easily into our solution.

  • Customer Experience
  • Productivity Improvement
  • Revenue Growth
  • Risk Reduction

Solution

Properstar modernized its analytics estate on Azure OpenAI Service, Azure AI Search, GitHub Copilot, moving from fragmented legacy tooling to a governed cloud data platform. Power BI then serves dashboards, self-service exploration, and decision support to business users at scale. It also connects with surrounding systems such as GitHub Copilot, Property listings and agency feeds, CRM and ERP systems, which lets the new Azure-based workflow fit into existing operations rather than standing apart from them. "The extensive Azure ecosystem allowed us to access cutting-edge AI tools and slot them easily into our solution.

Data flow

Content and operational context are prepared in Azure AI Search, passed to Azure OpenAI for generation or extraction, stored in Azure storage services, and returned through the user-facing application to employees or customers with grounded responses.

Solution architecture

5 components · 3 layers
  1. Compute
    • Azure OpenAI Service Runs large language model inference for summarization, extraction, conversational AI, and grounded answers.
  2. Serving
    • Azure AI Search Indexes enterprise content and retrieves grounding context for assistants and search experiences.
    • GitHub Copilot Accelerates software development by assisting engineers with code generation and editing.
    • Power BI Delivers dashboards, reports, and self-service analytics to business users.
  3. Orchestration
    • Power Platform Provides the low-code automation and application platform for workflow modernization.

Architecture clues

  • Applies retrieval-based grounding with Azure AI Search before answer generation.
  • Uses Azure OpenAI for grounded generation or large-scale extraction workloads.

Evidence from the source

"The extensive Azure ecosystem allowed us to access cutting-edge AI tools and slot them easily into our solution.
"We pushed the limits of what Azure AI Search could do and tested numerous approaches," Ivan Torreblanca, CTO of Properstar, recalls.
Gérard Paratte, CEO, Properstar More detail and better matches: Properstar relies on Azure AI technologies Using Microsoft Azure OpenAI Service is at the heart of Properstar's technological solution.
Using Azure AI Search, the developers then created a flexible filter system that lets users distinguish between mandatory and optional features.
Using Microsoft Azure OpenAI Service and Azure AI Search, Properstar analyzes unstructured real estate data, provides advanced filtering capabilities, and delivers relevant search results.
Transparency Evidence & methodology 96/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value96/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    4 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. Microsoft

    Properstar customer story

    First-party
    https://www.microsoft.com/en/customers/story/19986-properstar-azure-open-ai-service
    Source type
    Vendor-published customer story
    Published
    Dec 9, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Dec 9, 2024 · Microsoft · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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