{"id":2668,"date":"2026-04-02T18:01:45","date_gmt":"2026-04-02T16:01:45","guid":{"rendered":"https:\/\/evana.ai\/?p=2668"},"modified":"2026-04-23T14:18:40","modified_gmt":"2026-04-23T12:18:40","slug":"mongo-db","status":"publish","type":"post","link":"https:\/\/evana.ai\/eng\/mongo-db\/","title":{"rendered":"MongoDB \u2013 The database infrastructure behind EVANA"},"content":{"rendered":"

Why EVANA relies on MongoDB \u2013 and what that means for our customers<\/strong><\/h2>\n\n\n\n

<\/p>\n\n\n\n

Behind every EVANA analysis lies a conscious decision: the right database infrastructure. Because anyone who processes, classifies, and makes millions of documents searchable every day needs more than just a solid database \u2013 they need a platform that can keep pace with the demands of AI, scalability, and speed. Since 2023, EVANA has relied on MongoDB Atlas as its core database infrastructure. The result: a more stable, faster, and more precise platform for our customers \u2013 and a foundation that grows with our ambitions.<\/p>\n\n\n\n

<\/p>\n\n\n\n

\n
\n

Challenge<\/h3>\n\n\n\n

Tech sprawl, inconsistent data storage, and the limitations of relational databases slowed down the speed, stability, and scalability of the EVANA platform.<\/p>\n<\/div>\n\n\n\n

\n

Decision<\/h3>\n\n\n\n

EVANA migrated to MongoDB Atlas as a single source of truth in 2023 \u2013 for operational simplicity, flexibility and maximum developer efficiency.<\/p>\n<\/div>\n\n\n\n

\n

Result<\/h3>\n\n\n\n

Capacity for nearly one million files per day, 95 % accuracy over 38 million scanned pages \u2013 and a platform that delivers up to eight new releases daily.<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/p>\n\n\n\n

From Tech Sprawl to Single Source of Truth<\/h2>\n\n\n\n

<\/p>\n\n\n\n

In the first few years after its founding, EVANA's infrastructure grew organically \u2013 and with it, its complexity. PostgreSQL, Azure SQL, Elasticsearch, and MongoDB coexisted, data was replicated multiple times, and teams worked with different database logics. The result: rising costs, instability, and a platform that could no longer keep pace with the growing demands of AI processing. The decision to adopt MongoDB Atlas was the turning point: a unified, flexible platform that processes both structured and unstructured data \u2013 and is specifically designed for the requirements of modern AI applications.<\/p>\n\n\n\n

<\/p>\n\n\n\n

<\/p>\n\n\n\n

\n
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Three factors that tipped the scales<\/h3>\n\n\n\n

Operational simplicity, flexibility, and developer efficiency \u2013 these were the decisive criteria in the evaluation. MongoDB Atlas minimizes DevOps effort, enables rapid iteration, and supports all requirements from simple CRUD operations to vectorization and complex pipelines with a unified query language. <\/p>\n\n\n\n

For a growing AI company like EVANA, this is not a nice-to-have \u2013 it is a basic requirement.<\/p>\n\n\n\n

\n
Get to know EVANA now<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
\n
\"EVANA<\/figure>\n<\/div>\n<\/div>\n\n\n\n

<\/p>\n\n\n\n

Precision and speed \u2013 combined in one system<\/strong><\/h2>\n\n\n\n

<\/p>\n\n\n\n

EVANA now processes over 30,000 documents per hour using MongoDB Atlas. The document-based, schema-flexible data model is ideal for the heterogeneous data sets generated in the real estate industry: contracts, appraisals, land registers, technical plans, and ESG documents. All content is stored in a unified, fully searchable database \u2013 without the need for complex schema definitions for new document types.<\/p>\n\n\n\n

<\/p>\n\n\n\n

\n
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\"EVANA<\/figure>\n<\/div>\n\n\n\n
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Natural Language Search \u2013 directly on the documents<\/h3>\n\n\n\n

MongoDB Atlas Search allows EVANA customers to search their document repositories using natural language. <\/p>\n\n\n\n

Queries such as "all heating cost logs for the last three years" or "summary of all rental agreements" are executed directly on the original documents \u2013 without manual preparation, without detours. Fast, precise, and scalable.<\/p>\n\n\n\n

\n
Book a demo now!<\/a><\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

<\/p>\n\n\n\n

EVANA 2.0 \u2013 a platform that grows with our customers<\/strong><\/h2>\n\n\n\n

<\/p>\n\n\n\n

Since migrating to MongoDB Atlas, the EVANA platform has fundamentally changed. Instabilities and performance issues are a thing of the past. Instead of releasing every two months, we now deploy new features up to eight times a day \u2013 without impacting existing functionality. A single DevOps employee monitors performance and metrics. The team can focus on what matters most: developing, testing, and rapidly delivering new features.<\/p>\n\n\n\n

<\/p>\n\n\n\n

\u201e"With MongoDB, we've evolved into EVANA 2.0. It helps us keep up with the rate of change and deliver a great customer experience."\u201c<\/em><\/em><\/p>\n\n\n\n

Martin Teiber, CEO, EVANA<\/p>\n\n\n\n

<\/p>\n\n\n\n

More than a database \u2013 a foundation for the future<\/strong><\/strong><\/h3>\n\n\n\n

<\/p>\n\n\n\n

MongoDB Atlas is not only the foundation for today's EVANA platform \u2013 it's the basis for everything that's to come. The platform's flexibility allows for the integration of new document types without complex schema definitions, the rapid incorporation of new AI models, and the opening of new markets. Because AI is language-independent, EVANA can deploy its solutions to new regions and industries without modifications \u2013 from real estate to medical, legal, or public sector use cases.<\/p>\n\n\n\n

With OpenID Connect for secure authentication, complete API openness and an infrastructure that is always up to date, MongoDB Atlas creates the conditions for EVANA customers to rely on a stable, high-performance and future-proof platform today and tomorrow.<\/p>\n\n\n\n

<\/p>\n\n\n\n

About EVANA<\/strong><\/h3>\n\n\n\n

<\/p>\n\n\n\n

EVANA develops AI-powered solutions for analyzing large volumes of documents. Based on its own infrastructure, unstructured content is automatically recognized, extracted, and presented in a structured format. This gives companies faster access to relevant information and creates a reliable data foundation for analyses, processes, and decisions.<\/p>\n\n\n\n

<\/p>\n\n\n\n

<\/p>","protected":false},"excerpt":{"rendered":"

EVANA automates document processes and creates structure for faster, more informed decisions.<\/p>","protected":false},"author":8,"featured_media":2709,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"footnotes":""},"categories":[19],"tags":[],"class_list":["post-2668","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-use-case"],"yoast_head":"\nMongoDB \u2013 Die Datenbankinfrastruktur hinter EVANA - 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