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MongoDB Software / SaaS Germany

EVANA

Document Processing

EVANA needed to eliminate data sprawl and unstable SQL-based workflows that slowed document search, AI classification, and customer response times.

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Accuracy improved as the volume of documents increased from 6,000 to 30,000 per hour.
Accuracy reached 95% over 38 million scanned pages.
EVANA can deploy new features much faster, with up to eight releases a day.

Reported impact

Business outcome

EVANA now processes far more documents at higher accuracy, ships features multiple times a day, and gives customers faster, more reliable AI-powered property insights.

  • Cost Reduction
  • Customer Experience
  • Productivity Improvement

Solution

EVANA centralized its property-document platform on MongoDB Atlas after years of tech sprawl across PostgreSQL, Azure SQL, Elasticsearch, and other databases made data inconsistent and expensive to replicate for AI workloads. Atlas now serves as the single source of truth for structured and unstructured real-estate documents, while MongoDB Atlas Search powers natural-language and follow-up queries across data rooms and collections of millions of files. The platform also integrates OCR, AI classification, and OIDC-based identity controls so EVANA can ingest documents, categorize them, secure access, and release new features rapidly without creating new tables or schemas. This modernization dramatically improved throughput, accuracy, and release velocity while making the customer-facing service more reliable and cost effective.

Data flow

Documents enter EVANA through OCR and AI classification pipelines, are stored in MongoDB Atlas, queried via MongoDB Atlas Search, and shared through secure data rooms and natural-language workflows guarded by OIDC.

Solution architecture

3 components · 3 layers
  1. Storage
    • MongoDB Atlas Acts as EVANA's single source of truth for property documents and extracted metadata.
  2. Serving
    • MongoDB Atlas Search Supports natural-language and follow-up queries across transaction data rooms and document collections.
  3. Governance
    • OpenID Connect (OIDC) Secures human and machine identities without passwords.

Architecture clues

  • Atlas Search supports natural-language document queries and follow-up questions.
  • EVANA is phasing out PostgreSQL, Azure SQL, and other legacy databases in favor of one Atlas-based source of truth.
  • One DevOps resource now monitors performance and metrics while Atlas handles updates and maintenance.

Evidence from the source

Accuracy has improved as the volume of documents has increased from 6,000 to 30,000 per hour, reaching 95% accuracy over 38 million scanned pages.
From PostgreSQL to Azure SQL, Elastic Search and MongoDB, data was everywhere and inconsistent.
Using MongoDB Atlas Search, customers can run natural language queries on their data.
Transparency Evidence & methodology 100/100 reference value · 74% 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 value100/100
  • 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 specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence74%
  • 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 evidence15/20
    3 direct source quotes retained.
  • Date provenance0/10
    The publication date has no documented provenance.
  • 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. MongoDB

    EVANA customer story

    First-party
    https://www.mongodb.com/solutions/customer-case-studies/evana
    Source type
    Vendor-published customer story
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes

Date evidence

  • First archived Jan 3, 2026 · Internet Archive · Independent First Wayback Machine capture; confirms the page existed by this date, not when it was published.
    Open date source
  • Sitemap last modified Dec 2, 2025 · MongoDB · Publisher metadata Publisher-reported modification timestamp; not a publication date.
    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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