Microsoft Software / SaaS Germany

docunite

Document Processing

The challenge: The sheer number of files, systems, and stakeholders makes managing documents difficult

On average, some 60 percent of documents exist in more than one place.
“This results in elaborate file storage systems with up to 800 folders per property, of which 70 percent are empty and 10 percent are no longer accessible through the online structure,” Penn says.

Reported impact

Business outcome

“Thanks to Azure and Azure Cognitive Services, we’ve established a foundation and a solid data stock that will allow us to reap the vast benefits of AI in the future.”

  • Productivity Improvement

Solution

The challenge: The sheer number of files, systems, and stakeholders makes managing documents difficult Managing real estate also means managing piles of documents relating to each property in a way that’s always complete, transparent, and up to date. But reality often paints a vastly different picture: files are stored in a variety of systems, and both internal and external stakeholders require their own versions of the documents at varying points in the process. That’s why docunite harnessed the potential of Azure Cognitive Services to develop a solution that bundles all the industry’s requirements and automatically processes and classifies documents. In practice, however, this is often hard to achieve due to the rather fragmented nature of the industry. To coordinate renovation projects or find new tenants, real estate companies work with external stakeholders, who require access to those documents relevant to their role.

Data flow

Content from SharePoint is ingested into Azure OpenAI Service, where it is indexed, enriched, or reasoned over before grounded answers are returned to employees through business applications.

Solution architecture

3 components · 3 layers
  1. Compute
    • Azure OpenAI Service Provides large language model inference for natural language search, drafting, summarization, and recommendations.
  2. Storage
    • SharePoint Hosts source documents and collaboration content that downstream apps automate or analyze.
  3. Serving
    • Power BI Delivers dashboards, reports, and self-service analytics to business users.

Architecture clues

  • Azure OpenAI Service: Provides large language model inference for natural language search, drafting, summarization, and recommendations.
  • Integrates with SharePoint.
  • On average, some 60 percent of documents exist in more than one place.
  • Power BI: Delivers dashboards, reports, and self-service analytics to business users.
  • SharePoint: Hosts source documents and collaboration content that downstream apps automate or analyze.
  • “This results in elaborate file storage systems with up to 800 folders per property, of which 70 percent are empty and 10 percent are no longer accessible through the online structure,” Penn says.

Evidence from the source

In practice, however, this is often hard to achieve due to the rather fragmented nature of the industry. To coordinate renovation projects or find new tenants, real estate companies work with external stakeholders, who require access to those documents relevant to their role.
In the past, many real estate companies relied on generic digital document management systems (DMSs), adapting them to their specific requirements and spending a lot of money to keep them up-to-date. “That meant each real estate company was creating its own solution, even though they all had the same requirements.
Managing real estate also means managing piles of documents relating to each property in a way that’s always complete, transparent, and up to date. But reality often paints a vastly different picture: files are stored in a variety of systems, and both internal and external stakeholders require their own versions of the documents at varying points in the process.
The challenge: The sheer number of files, systems, and stakeholders makes managing documents difficult
“Thanks to Azure and Azure Cognitive Services, we’ve established a foundation and a solid data stock that will allow us to reap the vast benefits of AI in the future.”
Transparency Evidence & methodology 71/100 reference value · 87% 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 value71/100
  • 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 impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • 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 specificity8/10
    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

    docunite customer story

    First-party
    https://www.microsoft.com/en/customers/story/1734308506276180845-docunite-gmbh-azure-machine-learning-professional-services-en-germany
    Source type
    Vendor-published customer story
    Published
    Feb 20, 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 Feb 20, 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.