Resources

    Core Technology

    Four proprietary innovations, each a purpose-built answer to a real production limitation in existing vector databases.

    ProprietaryProduction-provenBenchmarked independently

    The Vector Graph Engine

    The Engine Behind the Numbers

    1

    Multi-Layer Memory Architecture

    Hot/warm/cold tiers keep frequently-accessed vectors in RAM and the rest on fast NVMe. Maximises throughput without over-provisioning memory.

    Enables 1B+ vectors on a single node

    2

    Efficient Quantization

    Scalar quantization (FP32 to INT8) compresses each vector 4x with near-zero recall loss. Endee quantizes at index-build time so queries always hit compressed vectors.

    1/10th the RAM, same 99%+ recall

    Endee

    powered by

    Vector
    Graph
    Engine

    Proprietary
    architecture

    3

    SIMD Acceleration

    CPU vector instructions (AVX-512, NEON) compute 16+ dot-products per clock cycle. Distance calculations run at hardware speed, no GPU, no special infrastructure needed.

    <5ms P99 latency at 10K+ QPS

    4

    Efficient Graph Construction

    Endee builds the HNSW graph incrementally with pruning and degree-limiting heuristics. Index builds 2-4x faster than naive HNSW, enabling real-time catalog updates.

    Live catalog sync without index rebuilds

    Four compounding innovations. Each one eliminates a bottleneck. Together, they deliver an unfair advantage.

    The Engine Underneath

    Vector Graph Engine (VGE)

    The Vector Graph Engine is Endee's unified search runtime, the layer that combines Layered Memory Architecture, Int8e Quantization, and Progressive Filtering into a single high-performance ANN pipeline. Rather than treating these as independent optimizations, VGE co-designs them so each reinforces the others: quantized vectors fit hotter graph layers into RAM, progressive filtering exploits the graph structure to enforce constraints early, and the memory hierarchy keeps hot nodes in cache for both techniques.

    90%

    Cost savings

    vs. traditional vector databases

    1/10th

    Infrastructure

    vs. equivalent memory footprint

    99%+

    Recall

    at one billion vectors

    Higher Recall

    Best-in-class accuracy at any scale

    Higher QPS

    More queries per second per node

    Lower Latency

    Sub-5ms p99 under load

    Layered Memory Architecture

    Int8e Quantization

    Progressive Filtering

    Queryable Encryption

    Core IP #1

    Layered Memory Architecture

    Hybrid storage for optimal cost-performance at scale

    Splits the HNSW graph across RAM and NVMe tiers. Hot nodes stay in memory, the rest stay cheap. Up to 10× less RAM with no compromise on recall.

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    Core IP #2

    Int8e Quantization

    4x compression with minimal recall degradation

    Proprietary 4× vector compression with per-vector dynamic calibration. Billion-scale indexes on commodity hardware, same 99%+ recall.

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    Core IP #3

    Progressive Filtering

    Multi-stage retrieval that preserves graph connectivity

    Applies metadata constraints incrementally during graph traversal so recall holds even under highly selective filters. No cliff-edge degradation.

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    Core IP #4

    Queryable Encryption

    Similarity search on encrypted vectors, zero decryption

    Similarity search directly on encrypted vectors. The server never sees plaintext: not at rest, not in transit, not during search. Built for HIPAA, GDPR, and defense requirements.

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    See the technology in production

    Independent benchmarks on the Cohere 10M dataset show Endee achieving the lowest cost-per-billion-queries of any tested vector database.

    View benchmarks Security overview