Results, validated

Proven results at scale.

Real-world implementations demonstrating Green Vectors across scale benchmarks, industry comparisons, and production deployments.

Case studies

Five deployments, five different constraints.

Scale and efficiency
Project Gutenberg
260GB reduced to 1.3GB across 50,000+ books. More than 15 million vectors consolidated to 76,000, with up to 59% better search quality.
Up to 99.5% storage reduction · Up to 4x faster retrieval
Read
Industry benchmark
Green Vectors vs. Elastic BBQ
A head-to-head benchmark against Elastic's Better Binary Quantization on the same Project Gutenberg dataset, under identical conditions.
2.1x higher accuracy · 116x more storage-efficient
Read
Production deployment
Enterprise Vector Reduction
Green Vectors applied at ingestion through the Kitana SDK for a multilingual document-retrieval platform, with no rip-and-replace and zero GPU dependence.
~89% storage reduced · ~9:1 consolidation ratio
Read
Semantic discovery
AI Patent Search
A legacy keyword-based patent search system moved from exact text matching to genuine conceptual understanding.
67% storage cost reduction · 10x faster search
Read
Enterprise performance
Sales Training Firm
A growing sales training organization scaled its RAG system without ballooning infrastructure costs or degrading answer quality.
76% database size reduction · 50% → 90% content accuracy
Read

Whether you're looking for enterprise search or building your own AI pipeline, let's discuss how Green Vectors applies to your workload.