Cut vector storage by up to 99.5% without trading away retrieval quality.
Kitana is the Green Vectors SDK. It eliminates redundant vectors at ingestion before they hit your retrieval stack. Drops in alongside Pinecone, Qdrant, Weaviate, or pgvector.
Not compression. Not another reranker.
PATENT-PENDING · 50,000+ BOOK BENCHMARK · UP TO 99.5% STORAGE REDUCTION · KITANA CLOSED BETA
Your vector database is probably storing the same meaning over and over.
Traditional RAG pipelines store every chunk, boilerplate section, duplicate, stale update, and near-identical record as another vector.
That works at small scale. Then the index grows, retrieval slows, costs become less predictable, and search quality gets polluted by redundant semantic noise.
The result: your AI system becomes more expensive and harder to trust as it learns more.
Keep your vector database. Reduce what surrounds it.
Most retrieval pipelines bolt on compression, hybrid search, rerankers, and knowledge graph layers to compensate for redundant vectors. Green Vectors removes the redundancy at ingestion, so those compensating layers become optional, not mandatory.
Your vector database stays. The bloat does not.
- Continuous Vectorizationstays current as data changes · no reindexing
- Megachunkingcaptures meaning across scales
- Auto Weightingamplifies high-signal content
Delivers graph-like retrieval — concept linking and semantic relationships — without operating a knowledge graph.
| Stack layer | Traditional pipeline | With Green Vectors |
|---|---|---|
| Vector database | Required | Required |
| Hybrid search layer | Required | Optional |
| Reranker | Required | Optional |
| Knowledge graph layer | Required | Optional |
| Re-embedding on update | On every change | Never |
| Application code changes | Yes | None |
The industry tried to make the warehouse smaller. We stopped storing the noise in the first place.
And as your data grows, your storage barely does. Traditional indexes scale linearly with content volume. Green Vectors scales with semantic novelty, not document count.
THE DISTINCTIONCompression and quantization shrink each vector. Green Vectors removes the vectors that shouldn't have been stored in the first place.
Vector bloat is not just a storage problem. It is a margin problem.
If every customer, document, event, and update expands your index, your AI product gets more expensive as it becomes more successful.
If your AI product depends on RAG, your vector layer is part of your margin structure.
Your AI margins should not collapse as usage grows.
- Larger indexes to store and maintain
- More vectors to search at query time
- More reranking and tuning work
- Higher infrastructure spend per query
- Less room to price competitively
Two ways to put Green Vectors to work.
From your data to cleaner retrieval, in one ingestion pass.
Drop in
Add Kitana alongside your existing vector database. Python SDK. No migration. No reindex.
from kitana import GreenVectors
gv = GreenVectors(vector_db="pinecone")
gv.ingest(documents)Reduce
Kitana groups related information by meaning and context using patent-pending methods. New information updates the relevant semantic representation without creating redundant vectors. Up to 99.5% less vector storage on benchmark workloads.
Retrieve
Query against a smaller, cleaner index. Up to 4x retrieval improvement at 15 million-vector scale. 25 to 59% accuracy lift on benchmark workloads.
Benchmark first. Integrate second.
No production migration required to evaluate.
Store meaning. Discard everything else.
Three patent-pending innovations.
Continuous Vectorization
Parent architecture. Identifies meaning-bearing concepts at ingestion and keeps the representation current as data changes. No reindex windows.
Fewer vectors per source, current as your data changes.
Megachunking
Adaptive semantic capture. Patent-pending methods that preserve deep context where fixed chunk sizes would lose it.
Higher recall, no chunk tuning required.
Auto Weighting
Relevance-aware ingestion. Amplifies signal as your corpus grows.
Reranker stack becomes optional, not mandatory.
Benchmarked against real workloads, not projections.
Smaller indexes. Faster retrieval. Better signal.
Project Gutenberg
- 200x storage efficiency
- 260GB → 1.3GB
- 15M+ vectors → 76K
- 25–59% accuracy lift
Green Vectors vs. Elastic BBQ
- 2.1x higher relevance
- Up to 77% faster queries
- 99% less storage
- 116x storage efficiency
Patent Search
- 10x faster conceptual retrieval
- 67% lower storage
- Relevance 45% → 87%
Results vary by corpus, retrieval workload, and integration pattern. That is why the first step is a benchmark, not a blind migration.
Retrieval is the wedge. Dynamic semantic data is the platform.
Green Vectors is designed for any system where semantic representations need to stay compact, current, and useful as data changes.
Edge AI
Memory-constrained environments such as devices, robotics, and embedded systems.
Real-time streaming
Sensor feeds, transaction logs, telemetry. Active design-partner focus.
Multimodal fusion
Text, sensor, and signal types in one semantic representation. Under development.
Continual learning
Models that adapt as data evolves. No retraining cycles. Emerging design-partner focus.
We are selecting design partners now.
Become a Design PartnerIf you build silicon, hardware, or platform infrastructure where retrieval performance and footprint matter, we should talk.
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