Map processes. Discover knowledge graphs. Zero-trust local execution.

Connect your systems, review what gets extracted, govern it with a versioned ontology, and explore it through process maps, process mining, and a grounded chat assistant -- deployed as one stack, on infrastructure you already control.

How data moves through the system

Every stage is a real, reviewable step. Nothing reaches your knowledge graph without a human approving it first.

STEP 1

Connect

GitHub, Confluence, NetSuite, S3, or a manual upload. Plus live observation capture for systems with no API at all.

STEP 2

Review & govern

Every document moves through Raw → Workspace → Canonical with a human at each layer, against a versioned, business-editable ontology.

STEP 3

Understand

An approved knowledge graph in Neo4j, rendered as process maps and mined for real bottlenecks. Never fabricated, always cited back to source.

STEP 4

Ask & act

A RAG chat assistant grounded in your graph, and discovery-then-replay automation that learns a procedure once and replays it deterministically.

What’s inside

Every capability below is real and runnable today. Not a roadmap slide.

Connectors & observation capture

Incremental sync from GitHub, Confluence, NetSuite, and S3. Or record a live browser session for a system with no exportable audit trail at all.

Document Vault

Git-backed, three-layer moderation (Raw → Workspace → Canonical) with full history, diff, and rollback before anything reaches the graph.

Ontology Studio

A versioned, business-editable schema. Propose from chat, import a DDL/YAML data model, or derive one deterministically from real spreadsheet structure.

Process maps & mining

Mermaid diagrams rendered directly from promoted graph data, plus a deterministic directly-follows-graph miner that flags real bottlenecks and deviations.

Discovery-then-replay automation

Four specialized agents learn a procedure once from a real browser session; a human approves exactly what it learned before it ever replays unattended.

Company Brain

Hybrid retrieval (vector + full-text) over your own graph, with citations checked against what was actually retrieved. Never an unsourced answer.

Vertical knowledge graphs

Split one graph into hard-scoped, department-level slices. Each with its own ontology scope, health metrics, and optional scoped agent pod.

Agent tools + MCP

A read-only query surface over the graph, exposed both as REST endpoints and a real MCP server for Claude Desktop, Cursor, and other MCP clients.

Sign in to your workspace

Your team’s connectors, ontology, and knowledge graph are already waiting.