Zuberi Vector

Semantic text representation built with your data, in your environment.

Zuberi Vector turns your proprietary documents into high-fidelity embeddings without ever leaving your cloud. Your models. Your cloud. Your control. Kervalt preserves that standard.

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Capabilities

Embeddings engineered for retrieval at enterprise scale

Text-to-vector embeddings

Convert text into dense vectors optimized for fast, precise semantic search across millions of documents.

Hidden pattern discovery

Surface relationships and themes buried in your corpus that keyword indexes structurally cannot see.

Accurate document comparison

Measure true semantic similarity between contracts, reports, and records — not just shared vocabulary.

Multimodal representations

Embed text alongside structured and visual content in a shared vector space for unified retrieval.

Specifications

Built for production retrieval workloads

Vector dimensions

1024

High-dimensional embeddings with configurable truncation, so you balance recall against storage and latency per index.

Throughput

Batch-first

Sustained high-volume embedding for bulk indexing, plus low-latency single queries for interactive search paths.

Multilingual support

23+ languages

Cross-lingual representations let a query in one language retrieve relevant documents written in another.

RAG-ready

Drops straight into your retrieval pipeline

Zuberi Vector emits standard dense vectors, so it plugs into the vector store you already operate — no proprietary lock-in, no translation layer. Index once, retrieve everywhere, and keep every embedding inside your security perimeter.

Pair it with Sankofa Rank to re-score retrieved candidates and lift precision on the queries that matter most to your business.

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Qdrant

Native dimension and distance-metric compatibility. Point your collection at Zuberi embeddings and query with your existing filters and payloads.

pgvector

Store embeddings next to your operational data in PostgreSQL — one backup policy, one access model, one compliance boundary.

Sovereign by default

Embedding jobs run in your VPC, on-premises, or in a Kervalt-managed Kanda Vault. Your corpus never trains anyone else's model.