Kitana implementation
Keep your RAG stack. Change what it has to carry.
Morphos works directly with your team to integrate Kitana into an existing RAG pipeline, establish your current baseline, and test Green Vectors against the same corpus and queries. Keep your embedding model, retrieval framework, and vector database. A migration is not required to evaluate the architecture.
Bring your current stack and a representative workload. We’ll determine whether there is enough redundancy, retrieval noise, or update overhead to justify a controlled Kitana evaluation.
Benchmark evidence
What has been measured so far.
Results depend on corpus, workload, configuration, and retrieval requirements. These are benchmark outcomes on specific datasets, not guarantees for every system.
- Up to 99.5%less vector storageProject Gutenberg benchmark
- Up to 59%better search qualityProject Gutenberg benchmark
- ~4xfaster queries at 15M-vector scaleGreen Vectors vs Elastic BBQ
Further evidence: Enterprise RAG: sales training firm, Enterprise vector reduction, Patent search.
How it fits
Your existing architecture stays recognizable.
Kitana fits between the embedding model and vector storage. Green Vectors changes the searchable representation before it enters the vector database, reducing redundant representations while preserving the original source material.
A controlled evaluation compares the current pipeline and the Kitana configuration using the same source material, queries, and success criteria.
- 01
Your source documents stay yours.
Kitana does not replace the original source material. It changes what enters the searchable layer, not what you keep.
- 02
Keep your embedding model.
You do not need to replace the embedding model simply to evaluate the architecture.
- 03
No database migration to run a test.
An evaluation runs against the vector database you already operate. Migration is a separate decision you may never need to make.
- 04
This is not compression or quantization.
Quantization reduces the precision of each vector. Green Vectors changes how many representations need to be stored and searched at all, by consolidating repeated meaning before it reaches the index.
Architecture review
Tell us what you are running.
Identity first, then a short set of questions about the system. If you are not close to the infrastructure, “Not sure” is a valid answer everywhere it appears.