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Build trusted knowledge systems

A self-hostable, knowledge-graph-grounded assistant and durable agent platform. A markdown knowledge base, an LLM-extracted entity graph with provenance, GraphRAG chat with citations, a provider-agnostic agent harness with guardrails and human-in-the-loop, evals with golden sets and drift detection, and client-facing visualizations — all multi-tenant and runnable on a laptop or a single VPS.

What eli.ai is

eli.ai turns a pile of markdown into a queryable, grounded knowledge system. Documents are chunked and indexed for full-text and vector search the moment you save them; embeddings and LLM entity extraction run asynchronously in the background. The result is a connected entity graph with provenance that chat and agents reason over — answering with citations that link straight back to the source chunk.

It is built for two audiences at once: personal local use — one command on a laptop, works offline — and consulting engagements, with per-client workspaces, interview-driven KB building, and gap analysis that tells you which business unit to interview next.

Explore the docs

The docs are organized into four tabs. Start wherever your question lives.

The eight capability systems

The Product tab is organized around eight named systems — one job each, carrying a document from a raw source to a governed, cited answer. Each links to its guide.

See how they chain end to end on the capability map

Start here

Three short reads that orient you before you dive into a tab.

Who it's for

Personal knowledge, self-hosted

One command on a laptop. Your vault stays on disk, git-able and Obsidian-compatible. Point it at a local Ollama model and everything runs offline — no API cost, no data leaving the box.

Consulting engagements

Per-client workspaces with hard tenant isolation, interview-driven KB building, agents that traverse the graph with approval-gated tools, and exportable deliverables with coverage and gap analysis for the room.

Ready to run it?

Follow the Quickstart to get a cited answer locally in a few minutes, read the Core principles for the ideas underneath it, or open the Architecture for the full system design and data-flow diagrams.