KINDX Knowledge Infrastructure

Example Use Cases

• 1

  Local-first, privacy-preserving contextual retrieval over enterprise documents.

• 2

  That require hybrid symbolic and neural search with LLM re-ranking on-device.

• 3

  Structured JSON/CSV/XML output and MCP protocol integration for seamless tool calls.

Description

KINDX is a high-performance backend designed for autonomous AI agents to perform deterministic, privacy-preserving contextual retrieval over enterprise corpora without data leaving the device. It combines BM25 full-text search, vector semantic retrieval, and LLM re-ranking using local GGUF models, ensuring data privacy and low latency. KINDX supports structured output formats and integrates tightly with the Model Context Protocol (MCP) for seamless agent communication, making it ideal for local-first, air-gapped AI deployments.