Learn about the latest in graph databases, AI-powered analytics, and transformative business intelligence solutions.
Thousands of public companies have real revenue, real filings, and zero analyst coverage. We built an open-source machine that turns their SEC filings into narrated research — and we run it in the open.
Double-entry bookkeeping is one of the great inventions in the history of business. But the journal entry is a derived artifact, not the source of truth. RoboLedger records the economic event first and derives the books from it—so the meaning survives all the way to the balance sheet.
A published financial report should be something you can read with your eyes, query with SPARQL, and validate with SHACL—without loading a single specialized tool. So we built a proof of concept: our reports, rendered as DataBooks.
A ledger and a financial report are two shapes of the same truth. Here is the single primitive that connects them—and that lets you both pivot a statement out of your transactions and author a schedule into them.
Every public company's financials are already public—and almost nobody can actually use them. Here's the open, queryable graph we built on top of SEC XBRL, and what it unlocks.
People assume "standardized financial reporting" means rigid, one-size-fits-all forms. It does not. A standard fixes the structure—the shared vocabulary and how it rolls up—while your content stays entirely yours.
Financial analysis needs both the numbers and the story behind them. We built a platform that bridges structured XBRL facts in a knowledge graph with full-text search across SEC filing narratives—and lets AI chain both together.
Most AI projects fail because the data isn't ready. Context graphs are the semantic layer that transforms scattered financial data into meaning AI can actually understand.
The modern data stack is collapsing under its own weight. We built RoboSystems from the ground up for what comes next: unified databases, event-driven orchestration, and AI-native semantics.
Your business runs on relationships—between customers and revenue, costs and activities, risks and opportunities. So why does your financial system pretend these connections don't exist?
What if your AI could actually execute financial analysis instead of just talking about it? Model Context Protocol makes AI agents that do real work, not just generate text.
Why the future of business intelligence lies not in isolated metrics, but in the rich, interconnected relationships between financial outcomes and the operational activities that drive them.
