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RoboSystems is an enterprise-grade financial knowledge graph platform that transforms complex financial and operational data into actionable intelligence through graph-based analytics and AI-powered insights.
This wiki is the technical documentation for using, operating, and building on the platform. It is organized into six areas — orientation and self-hosting, hands-on demos, the operational API, the RoboLedger and RoboInvestor extensions, the financial-content fabric, and the document and search layer.
Orientation and self-hosting: stand up a local stack, learn the vocabulary, and understand the system design.
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Quick Start - From
just startto your first authenticated query in ten minutes - Core Concepts - The vocabulary: graphs, tiers, blocks, operators, operational vs analytical
- Architecture Overview - System design and components
- Bootstrap Guide - Set up AWS OIDC federation and GitHub Actions (self-hosting)
Hands-on walkthroughs — run one command, explore the result.
- RoboLedger Demos - Synthetic-data and Seattle Method XBRL demos
- SEC XBRL Pipeline - Load and query real SEC financial filings
- Custom Graph Schema - Design, build, and query a custom graph
The core platform API and graph management.
- Authentication & API Keys - The X-API-Key header for technical access, and the JWT boundary
- Graphs & Multi-Tenancy - The graph_id model, tiers, and subgraphs
- Graph Operations - The CQRS operation surface: backups, subgraphs, materialize
- Querying the Analytical Graph - Ad-hoc Cypher over the analytical (OLAP) graph
- Credits & Billing - The credit model: only AI operations consume credits
- AI Operators & MCP - The MCP surface and the three retrieval planes
- Pipeline Guide - Data pipelines, Dagster architecture, and custom adapters
RoboLedger and RoboInvestor — the graph-scoped product surfaces.
- Extensions Surface Overview - The URL topology, three sub-surfaces, and feature flags
- GraphQL Reads - Ad-hoc typed reads over the operational (OLTP) graph
- RoboLedger Operations - Command writes and analytical views (the fact grid)
- RoboInvestor Operations - Portfolios, securities, and positions
- Connecting QuickBooks Locally - Run real QB OAuth against a local stack via ngrok
The semantic content layer — how taxonomy, ledger, and report content is modeled and contributed.
- Information Blocks - The unifying envelope that ties everything together
- Taxonomy & Frameworks - The rs-gaap and fac frameworks, the library, and the Taxonomy Block
- Event-Driven Ledger - REA, the three-level ledger, and the Event Block
- Reporting & Rendering - The fact grid engine, reporting styles, and views
- Serialization & Export - Exporting blocks to XBRL and JSON-LD
Unstructured content and retrieval — the institutional-knowledge layer that grounds AI.
- Search & AI Retrieval - Index documents and retrieve them through MCP tools
- Document Management - The document store over entity graphs
- File Uploads - File uploads for generic graphs
- Component READMEs - Detailed technical docs in the codebase
- API Documentation - API reference with machine-readable OpenAPI spec
© 2026 RFS LLC
- Authentication & API Keys
- Graphs & Multi-Tenancy
- Shared Repositories
- Graph Operations
- Querying the Analytical Graph
- Credits & Billing
- AI Operators & MCP
- Pipeline Guide
- Extensions Surface Overview
- GraphQL Reads
- RoboLedger Operations
- RoboInvestor Operations
- Connecting QuickBooks Locally