case-study-anima-images

qdrant-landing/content/blog/case-study-anima-health.md

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 title: "How Anima Health scaled clinical document intelligence with Qdrant"
 short_description: "Anima Health scaled privacy-first clinical intelligence with Qdrant."
 description: "Discover how Anima Health used Qdrant to power vector search for clinical document coding, privacy-first retrieval, and agentic workflows in UK primary care."
-preview_image: /blog/case-study-anima-health/social_preview_partnership-anima-health.jpg
-social_preview_image: /blog/case-study-anima-health/social_preview_partnership-anima-health.jpg
+preview_image: /blog/case-study-anima-health/social_preview_partnership-anima-health.png
+social_preview_image: /blog/case-study-anima-health/social_preview_partnership-anima-health.png
 date: 2026-01-28
 author: "Daniel Azoulai"
 featured: true
@@ -20,6 +20,8 @@ tags:
 - case study
 ---
 
+![Anima Health scaled privacy-first clinical intelligence with Qdrant](/blog/case-study-anima-health/anima-bento.png)
+
 Primary care systems across the UK are under intense strain. General practitioners (GPs) face overwhelming patient demand, chronic understaffing, and a growing administrative burden that limits the time they can spend delivering care. <a href="https://animahealth.com/" target="_blank">Anima Health</a> set out to address this challenge by building a clinical operating system designed to make primary care more efficient, more informed, and more humane for both clinicians and patients.
 
 At the heart of Anima’s platform is the ability to process large volumes of unstructured clinical data, including documents, test results, referral letters, and notes, while maintaining strict privacy guarantees. To achieve this at scale, Anima relies on Qdrant as a core infrastructure component for vector search, similarity analysis, and agentic AI workflows.