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vkxai

Cross-industry · Vector-native · Enterprise AI

Embeddings · similarity · retrieval — meaning in high-dimensional space.

Vector KnowledgeX
for the enterprise

VKX delivers vector knowledge infrastructure for cross-industry enterprises: semantic search and retrieval-augmented AI that connects data from any line of business to defensible answers—with consistency, accountability, and controls that scale across sectors.

Explore the platformTalk to VKX
768-dim
Semantic representation
k-NN + RAG
Grounded answers
Cross-industry
One retrieval spine

Architecture

From raw data to answers

Encode multimodal inputs into embeddings, store and search in a vector database, then apply VKX so assistants and apps retrieve grounded knowledge—not guesses.

Data → Encoders → Vectors

[0.31, 0.42, …] · [0.12, −0.51, …]

Vector database

Indexed embeddings · similarity search

VKX
VectorKnowledgeX

Assistants & apps

  • Copilots
  • Search
  • Agents

Built for production AI—not demos

The same vector-native spine powers accurate retrieval whether your workloads are clinical evidence, capital markets policy, or engineering runbooks.

  • Semantic retrieval

    Find meaning—not just keywords—across documents, tickets, and systems of record so teams reach defensible context faster.

  • Grounded generation

    Keep assistants and workflows anchored to sources you trust with patterns designed for evaluation and operational review.

  • Cross-industry discipline

    One retrieval backbone for regulated and high-velocity units alike: shared governance, domain-specific authority.

From language to geometry

Embeddings turn words, passages, and multimodal signals into arrays models can compare mathematically. Similar ideas cluster together—so "apple" sits closer to "pear" than to "car"—powering semantic search and grounded answers at enterprise scale.

Cross-industry by design

Depth lives in industry-specific programs—not a single-vertical story. Explore how teams adopt vector knowledge in their context.

Frequently asked questions

What is vector knowledge?
Vector knowledge means representing text and other data as numerical embeddings so AI systems can measure semantic similarity: related concepts sit closer in vector space than unrelated ones, enabling meaning-aware search beyond exact keyword match.
How does VKX help enterprises?
VKX focuses on semantic retrieval and retrieval-augmented workflows so copilots and search stay grounded in your approved sources—supporting accuracy, attribution, and governance across industries.
What is retrieval-augmented generation (RAG)?
RAG retrieves relevant context from a knowledge base (often vector-backed) before the model generates an answer, reducing reliance on memorized weights and improving factual grounding when implemented with strong evaluation and access controls.

Enterprise knowledge, semantically connected—across industries.

VKX helps cross-industry enterprises govern and activate institutional knowledge with vector-native AI—so retrieval, copilots, and analytics stay accurate, attributable, and production-ready everywhere you operate.

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