| Our Customers Words |
| name |
position |
review |
avatar |
logo |
| Jordan Parker |
Co-Founder of Lawme.ai |
“The more data you feed into the agent, the better it gets. But to truly scale, you need a vector database that maintains low latency, high accuracy, and keeps costs in check. Qdrant makes that possible.” |
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alt |
| /img/customers/jordan-parker.svg |
Jordan Parker Avatar |
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| src |
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| /img/brands/lawme-ai.svg |
Lawme.ai Logo |
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| name |
position |
review |
avatar |
logo |
| Justin Mack |
Co-founder, CTO |
“Our customers need to compare millions of possible patent–product pairings in seconds, not days - that means vector search that can handle huge data sets and surgical-grade HNSW filtering.” |
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alt |
| /img/customers/justin-mack.svg |
Justin Mack Avatar |
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| src |
alt |
| /img/brands/garden.svg |
Garden Logo |
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| name |
position |
review |
avatar |
logo |
| Lesly Franco |
CTO, Aracor |
“Search is a massive problem. Our platform ingests thousands of legal documents, each requiring precise retrieval and accurate citations. Without Qdrant, delivering this level of performance and scale was nearly impossible.” |
| src |
alt |
| /img/customers/lesly-franco.svg |
Lesly Franco Avatar |
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| src |
alt |
| /img/brands/aracor.svg |
Aracor Logo |
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