Merge pull request #1633 from qdrant/case-study-garden

case-study-garden

qdrant-landing/content/blog/case-study-garden-intel.md

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+---
+draft: false
+title: "How Garden Scaled Patent Intelligence with Qdrant"
+short_description: "Garden unlocked patent analysis by migrating to Qdrant’s filterable vector search."
+description: "Discover how Garden ingests 200 M+ patents and product documents, achieves sub-100 ms query latency, and launched a new infringement-analysis business line with Qdrant."
+preview_image: /blog/case-study-garden/social_preview_case-study-garden.jpg
+social_preview_image: /blog/case-study-garden/social_preview_case-study-garden.jpg
+date: 2025-05-09T00:00:00Z
+author: "Daniel Azoulai"
+featured: true
+
+tags:
+- Garden Intel
+- vector search
+- patent analysis
+- intellectual property
+- case study
+---
+
+## Garden Accelerates Patent Intelligence with Qdrant’s Filterable Vector Search
+
+![How Garden Unlocked AI Patent Analysis](/blog/case-study-garden/case-study-garden-bento-dark.jpg)
+
+For more than a century, patent litigation has been a slow, people-powered business. Analysts read page after page—sometimes tens of thousands of pages—hunting for the smoking-gun paragraph that proves infringement or invalidity. Garden, a New York-based startup, set out to change that by applying large-scale AI to the entire global patent corpus—more than 200 million patents—in conjunction with terabytes of real world data.
+
+*“Our customers need to compare millions of possible patent–product pairings in seconds, not days,” explains co-founder Justin Mack. “That means vector search that can handle huge data sets and surgical-grade filtering.”*
+
+### A data set that breaks naïve vector search
+
+Each patent can run to 100+ pages and, thanks to decades of revisions, carries roughly        2,000 metadata fields: jurisdiction, grant date, family ID, claim dependencies, and so on. Garden splits every patent into semantically meaningful chunks, producing “many hundreds of millions” of vectors. The same pipeline ingests real-world product data to compare against the patents.
+
+The engineering demands quickly outgrew Garden’s first solution, a fully-managed vector service. They had tens of gigabytes of data already costing ≈ $5,000 / month. And a lack of native filterable-HNSW meant that Garden had to stand up a separate index for every combination of country, date range, and technology tag. Finally, with no infrastructure visibility, troubleshooting was slow and expensive.
+
+A second migration to a self-hosted open-source alternative cut costs but introduced new pains: on-call operations for a two-person team, upgrades during business hours, and—crucially—the same filtering limitations.
+
+### Discovering Qdrant
+
+When Garden found Qdrant’s blog post on filterable HNSW, the team realized they could get the search semantics they wanted without bolting on bespoke sharding logic.
+
+*“Filterable HNSW was the deal-maker, but Qdrant Cloud’s *managed* Rust backbone sealed it,” says Mack. “We kept source-level transparency while off-loading 24×7 ops.”*
+
+* **Scalar quantization (8-bit)** keeps hot vectors in RAM while colder, full-precision embeddings sit on disk—perfect for Garden’s read-heavy, bursty workload.
+
+* **SLA-backed sub-100ms latency** meets Garden’s product target even when a user fires off thousands of queries in a single button-click.
+
+* **Pay-for-what-you-use pricing** lets Garden store 10× more data for roughly the same cost it once paid for a fraction of the corpus.
+
+### Migration in practice
+
+Garden already held all vectors in Google Cloud Storage. A weekend of scripted ETL pushed the embeddings into Qdrant Cloud. Because Qdrant’s ingestion API mirrors popular open-source conventions, the team only altered a few lines of an existing migration script. The heaviest lift—GPU-based embedding of 200M patents—was finished months earlier on a 2,000-GPU transient cluster.
+
+### **Business impact**
+
+| KPI | Before Qdrant | After Qdrant |
+| ----- | ----- | ----- |
+| Addressable patent corpus | ≈ 20M | **200M+** |
+| Vector data under management | tens of millions | **hundreds of millions** |
+| Typical query latency | 250 – 400ms | **\< 100ms p95** |
+| Cost per stored GB | baseline | **\~ 10× lower** |
+| New revenue lines | 0 | **Full infringement-analysis product** |
+
+Filterable HNSW didn’t just speed up existing workflows; it unlocked an entirely new line of business—high-confidence infringement detection. Clients now click a button and receive a claim-chart quality analysis in minutes. For some enterprises that translates into seven-plus-figure licensing wins or decisive defense against patent trolls.
+
+### Looking ahead
+
+As Garden’s customer base grows, query-per-second (QPS) requirements will rise faster than data volume. Meanwhile, Garden plans deeper enrichment of every patent—breaking long descriptions into structured facts the vector index can exploit.
+
+*“We don’t have to think about the vector layer anymore,” Mack notes. “Qdrant lets us focus on the IP insights our customers pay for.”*