KITANA :: CLOSED BETA — 10 DESIGN PARTNER SLOTS

    Your RAG Pipeline is 99.5% Waste.
    Fix It in One Integration.

    Kitana is the Python SDK powered by Green Vectors technology. It drops into your existing pipeline between your embedding model and your vector database, eliminates redundant vector storage, and delivers measurably better retrieval accuracy — through a single gRPC call.

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    INTEGRATION SPOTLIGHT

    From Proof-of-Concept to Production

    USE CASE

    Enterprise Document Retrieval

    50,000-book corpus from Project Gutenberg — the most comprehensive open-domain retrieval benchmark available.

    INTEGRATION
    Single-day POC
    3 API calls in the pipeline
    No infrastructure changes
    RESULTS
    99.5%less vector storage
    +25–59%retrieval accuracy
    ~4xfaster at 15M-vector scale
    THE PROBLEM

    Your Vector Database Has a Physics Problem

    Traditional RAG systems store everything — fonts, headers, boilerplate, duplicate concepts, formatting noise. The result is vector databases where the vast majority of what's stored adds nothing to retrieval quality. It just adds cost.

    As your corpus grows, this compounds. Storage costs scale linearly. Query latency increases. Accuracy degrades because your retrieval step is swimming through noise to find signal.

    The standard industry response is compression and quantization. These trade accuracy for cost savings. You're forced to choose between "expensive and accurate" or "affordable and unreliable."

    Kitana eliminates that tradeoff entirely.

    STORAGE WASTE
    99.5%

    of vectors add zero value

    INTEGRATION

    Three Steps. Your Existing Stack.

    Kitana sits between your embedding model and your vector database. During ingestion, it processes your vectors through Green Vectors technology.

    EXTRACT & WEIGHTREADY
    01

    Extract & Weight

    Kitana's relevance-aware ingestion pipeline analyzes your corpus and scores semantic significance per chunk using patent-pending weighting methods. Batch-optimized.

    KITANARIZEPROCESSING
    02

    Kitanarize

    Send your weighted embeddings to Kitana's gRPC endpoint. The engine returns optimized vectors that preserve full semantic meaning in a fraction of the original storage footprint.

    STORE & QUERYACTIVE
    03

    Store & Query

    Write the resulting vectors to any Euclidean-distance vector database — Qdrant, Pinecone, Weaviate, pgvector. Query as normal. Get better results.

    WHAT YOU KEEP
    Embedding ModelsVector DatabaseLLMRetrieval LogicYour Entire Stack
    BENCHMARKS

    The Numbers Don't Lie

    STORAGE REDUCTION
    99.5%

    260GB → 1.3GB

    50,000 books reduced from 15 million vectors to 76,000. Not lossy compression — semantic transformation that eliminates redundancy at the concept level.

    ACCURACY IMPROVEMENT
    up to 59%

    2.1x better than Elastic BBQ

    25 to 59% lift over baseline, validated across multiple domains and benchmarks. Fewer vectors doesn't mean less information. It means less noise competing with your actual signal.

    FASTER RETRIEVAL
    ~4x

    at 15M-vector scale

    Pre-indexed semantic concepts complete retrieval in a fraction of conventional latency budgets. Measured at 15M-vector scale.

    SDK :: PRODUCTION-READY

    Built for Production

    Kitana ships as a Python SDK (3.11+) with both synchronous and asynchronous clients. Authentication, token renewal, and client-side validation are handled automatically.

    The SDK validates requests locally before sending — checking minimum dimensions, data integrity, and structure — so you catch issues before they cost you a network round trip.

    Retries are built in with exponential backoff for transient failures.

    SPEC SHEET
    Python 3.11+ (CPython 3.11–3.13)
    gRPC transport with auto retry & backoff
    Patent-pending weighting and semantic transformation methods
    Async-first client with sync support
    Client-side validation before network calls
    Drops in alongside Pinecone, Qdrant, Weaviate, pgvector, and similar Euclidean-distance vector DBs
    Batch processing for large-corpus ingestion
    APPLICATIONS

    Where Kitana Delivers

    Enterprise Knowledge Bases

    Companies with 100K+ documents hitting accuracy walls and storage budgets simultaneously. Kitana makes retrieval dramatically more precise without infrastructure overhaul.

    RAG-Powered Products

    SaaS companies embedding AI search into their products. Kitana's efficiency translates directly into margin advantage.

    Regulated Industries

    Legal, healthcare, and financial services where retrieval accuracy isn't optional.

    Real-Time Systems

    Fraud detection, dynamic pricing, and personalization engines where speed is critical.

    AI Infrastructure Teams

    Platform engineers building shared infrastructure for multiple internal teams.

    KITANA :: ENTERPRISE

    Enterprise Licensing

    Kitana is available through annual enterprise licensing, structured around your deployment scale, corpus size, and integration requirements. We're also exploring usage-based models for mid-market deployments.

    During closed beta, we're working closely with a select group of design partners who get hands-on integration support and influence the SDK roadmap.

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    FAQ :: DEVELOPERS

    Common Questions

    No. Kitana processes vectors after your embedding model generates them. Use whatever model works for your domain — OpenAI, Cohere, Sentence Transformers, custom fine-tuned models. Kitana optimizes the output.
    Any vector database configured for Euclidean distance search. Validated against Qdrant, Pinecone, Weaviate, and pgvector. If you're using something else, reach out and we'll confirm compatibility.
    Most teams have a working proof-of-concept within a day. The SDK drops into existing ingestion pipelines with minimal refactoring. Full production timelines depend on corpus size and your deployment process.
    We recommend keeping original embeddings and source text in cold/warm storage during beta. As the weighting engine evolves, you may want to reprocess. Your original data is never modified.
    The Kitana engine processes your vectors via gRPC. Your source text and documents remain in your infrastructure. Only vector data transits to our engine for optimization and is returned immediately. We do not store your vectors or source content.
    Green Vectors technology is patent-pending, with multiple filings covering the core methodology and continuation applications expanding the IP portfolio. Licensing Kitana includes the legal right to deploy Green Vectors in your infrastructure.
    3 OF 10 DESIGN PARTNER SLOTS REMAINING

    Stop Paying for
    Dimensions That Don't Matter.

    If you're running a RAG pipeline and feeling the pain of vector bloat, cost scaling, or accuracy degradation — we want to talk.

    Request Kitana Access