Platform

The VKX platform, deployed where your data lives

VKX is software, not a hosted black box: a platform that governs, secures, and observes every dimension of your LLM and agent workloads—vectors, retrieval, tools, and actions—installed inside your environment by forward-deployed engineers.

Govern: Vectors, sources & authority
Secure: Access, residency & audit
Observe: Every retrieval & tool call

The suite

Each module closes a structural gap the market has stopped arguing about: retrieval that leaves the cloud, retrieval that stays correct, retrieval agents can be trusted with, and retrieval that survives agent-scale load.

Deploy anywhere
VKX Sovereign Edge™
Cloud-native vector stacks can't serve air-gapped, offline, or latency-critical environments—clinical settings, manufacturing floors, vehicles, sovereign clouds.
Offline-first vector search and local inference that runs where the data is—on-device indexes and on-premises configurations with sync-when-connected reconciliation back to the core platform, aligned to your HIPAA, GDPR, and residency posture.
- Lightweight on-device indexes
- Sync-when-connected architecture
- Air-gapped & sovereign deployments
Retrieval lifecycle
VKX RAGOps Orchestrator™
Retrieval quality degrades silently as sources, embedding models, and chunking strategies change—and LLMOps tools watch prompts and serving, not the retrieval lifecycle.
Automated lifecycle management for production RAG: continuous evaluation at module, component, and system level, with drift detection across query, retrieval, and generation—so recall drops surface as alerts, not incidents.
- Three-tier automated evaluation
- Semantic & vocabulary drift detection
- Continuous coverage audits
Governed context
VKX Hybrid Enterprise Graph™
Pure vector similarity can't express ownership, entitlements, or explicit relationships—so security teams can't trust it, and agents can't safely act on it.
A shared context layer that fuses knowledge-graph structure with semantic vectors: retrieval scoped by the graph, entitlements enforced at query time, and every answer explainable through lineage back to its sources.
- Graph-scoped semantic search
- Permission-aware entitlements
- Lineage-based explainability
Agentic scale
VKX AgentCache™
Autonomous agents issue an order of magnitude more queries than human applications—exhausting connection pools and turning usage-based costs volatile.
A semantic caching gateway built for agent loops: semantically equivalent query-answer pairs are reused at the retrieval layer, connection pools size elastically, and consumption becomes predictable, reserved-capacity performance.
- Semantic query-answer caching
- Elastic connection pooling
- Reserved-capacity cost control

How it works

01 · Ingest

Represent

Normalize documents, messages, and structured records into embeddings and metadata your teams can govern.

Multimodal encoders
Chunking & metadata
Lineage preserved

02 · Retrieve

Index

Similarity and hybrid retrieval tuned for precision—so the right passages surface before generation or ranking.

k-NN + hybrid
Access-aware filters
Latency budgets

03 · Ground

Observe

Evaluation hooks, logging, and human-in-the-loop patterns that fit how security and platform teams ship AI.

Eval harness
Trace + attribution
Human review lanes

Principles that scale past the demo

Semantic-first

Vector-native by default, tuned to the vocabulary and risk profile of each line of business.

Governed by design

Access, residency, and audit patterns wired through retrieval—not bolted on after the fact.

Operationally measured

Retrieval quality, groundedness, and failure modes observable with the rigor your platform team expects.

Deployment model

The platform runs in your cloud or on-premises, exposes your knowledge as MCP tools, and is installed by forward-deployed engineers who stay until your team owns it.

Scope a path to production