Kitana for Existing Vector Databases
Kitana is a Python SDK that applies patent-pending Green Vectors technology at the ingestion layer, alongside the vector database you already use. It does not replace Pinecone, Qdrant, Weaviate, or pgvector. It optimizes what enters them. Your database, your query path, and your embedding model stay exactly as they are. What changes is that the index becomes dramatically smaller and cleaner, which lowers cost, improves latency, and improves accuracy.
How Kitana fits into your existing stack
In a typical pipeline, your embedding model produces vectors that are written directly to your vector database. Kitana adds one step at ingestion: data is processed through Kitana before it is written. Green Vectors eliminates redundant vectors at this stage, so the database stores far fewer of them. Retrieval then runs against this smaller, cleaner index exactly as before. There is no change to how your application queries the database.
Supported vector databases
Kitana works alongside any Euclidean-distance vector database, including Pinecone, Qdrant, Weaviate, and pgvector. You keep the database you have chosen, including its managed infrastructure, performance characteristics, and tooling.
What changes and what does not
What does not change: your vector database, your query path, your integration code, and your embedding model. Use whatever embedding model suits your domain. Kitana optimizes the vectors that model produces; it does not replace it. What changes: the number of vectors stored drops by up to 99.5% in benchmarked workloads, storage and query costs fall accordingly, and first-pass retrieval quality improves.
What becomes optional
Because the index is clean and accurate from ingestion, the auxiliary infrastructure teams normally add to compensate for noisy retrieval often becomes unnecessary. Separate reranking stages, parallel keyword pipelines maintained for hybrid search, and scheduled reindex jobs are frequently no longer required for production workloads. Ultra-high-precision applications can still layer a reranker on top, and Kitana remains compatible with any reranker.
Getting started
Kitana is in closed beta and available through enterprise licensing structured around deployment scale, corpus size, and integration requirements. Design partners receive hands-on integration support and direct access to the engineering team.