---
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.”*