Update case-study-dragonfruit.md

qdrant-landing/content/blog/case-study-dragonfruit.md

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 ![Dragonfruit Overview](/blog/case-study-dragonfruit/dragonfruit-bento-box-dark.jpg)
 
 ## Dragonfruit AI scales real-time computer vision with Qdrant
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 ### Introduction
 
-Dragonfruit builds enterprise-ready computer vision solutions, turning ordinary IP camera feeds into actionable insights for security, safety, operations, and compliance. Their platform ships a suite of AI “agents,” including retail loss prevention and warehouse safety, that run with a patented “Split AI” approach: real-time inference on-prem for speed and bandwidth efficiency, paired with cloud services for aggregation and search. The business imperative was clear: keep total cost of ownership low, meet strict latency targets, and operate reliably across hundreds of sites with thousands of cameras, without asking customers to rip and replace existing infrastructure.
+<a href="https://www.dragonfruit.ai/" target="_blank">Dragonfruit AI</a> builds enterprise-ready computer vision solutions, turning ordinary IP camera feeds into actionable insights for security, safety, operations, and compliance. Their platform ships a suite of AI “agents,” including retail loss prevention and warehouse safety, that run with a patented “Split AI” approach: real-time inference on-prem for speed and bandwidth efficiency, paired with cloud services for aggregation and search. The business imperative was clear: keep total cost of ownership low, meet strict latency targets, and operate reliably across hundreds of sites with thousands of cameras, without asking customers to rip and replace existing infrastructure.
 
 *“We use customers’ existing camera networks and deliver the lowest total cost of ownership we can. On-prem inference plus smart use of the cloud is what makes it practical at retail scale.”*  
  — Karissa Price, Chief Customer Officer, Dragonfruit AI
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 ### The challenge: Real-time at messy, planetary scale
 
 Retail and warehouse environments are bandwidth-constrained and heterogeneous. A single store may run 25–130 IP cameras, and many Dragonfruit agents are real-time, for example burglar alarms or self-checkout monitoring. Engineering needed to:
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 * Sustain high ingestion and high query throughput simultaneously, with strict tail latencies.
 
 * Operate a vector store at enterprise scale: thousands of locations → thousands of cameras, accumulating into tens to hundreds of billions of vectors and multi-terabyte storage.
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 ### Why Qdrant: Performance headroom and operational control
 
 Dragonfruit chose the open-source version of Qdrant as its vector database to meet the twin pressures of real-time reads and high-velocity writes. In head-to-head experiments, Qdrant delivered the QPS targets they needed while giving the team granular, [per-collection](https://qdrant.tech/documentation/concepts/collections/) tuning to match workload diversity.
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 *“With Qdrant’s collection-level controls, we matched very different workloads, some ingestion-heavy, some read-intensive, and still hit real-time query performance.”*  
  — **Shivang Agarwal, VP Engineering, Dragonfruit AI**
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 ### Solution architecture: Split AI \+ vector search
 
 At the edge, Mac Minis ingest RTSP streams from IP cameras, run on-prem inference, and emit compact embeddings and event metadata. In the cloud, Qdrant serves multiple, distinct retrieval patterns:
@@ -67,7 +63,6 @@ At the edge, Mac Minis ingest RTSP streams from IP cameras, run on-prem inferenc
 * **Self-checkout product verification.** Frames around scan events are embedded and matched against clustered product libraries to detect missed or incorrect scans in real time.
 
 * **Full-frame semantic search.** Video frames are embedded for [text-to-image retrieval](https://qdrant.tech/advanced-search/) (for example “emergency door open”), enabling rapid incident triage and safety reviews.
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 ### Results: New agents, faster delivery, lower TCO
 
 Qdrant became an enabling layer for Dragonfruit’s agent roadmap. With real-time retrieval performance and cost-efficient storage, the team launched and iterated on new domain-specific agents more quickly, spanning loss prevention, occupational safety, and warehouse operations.
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 * Real-time processing of many cameras per site at high FPS on edge, with only inference data transmitted.
 
 * Format optimization: float16 embeddings standard for most pipelines to reduce memory and improve throughput while maintaining retrieval quality.
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 ### Lessons learned 
 
 Operating retrieval for vision at enterprise scale is as much an operations problem as an algorithms problem. Dragonfruit learned to tune per workload, not per system. Treat each collection as a workload with its own performance profile; shard counts, flush intervals, and vector precision matter.
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 ### Conclusion
 
 Dragonfruit shows how a Split-AI architecture plus a purpose-built vector database can turn ubiquitous cameras into reliable, low-latency analytics at enterprise scale. Qdrant’s performance headroom, per-collection controls, and operational simplicity helped the team meet real-time constraints and expand their agent portfolio without ballooning costs or bandwidth. As the platform grows, tighter, native data-lifecycle tooling will further reduce operational toil, but the core result is already clear: fast, affordable, and scalable computer vision for the real world.