FinCatch is building a graph-grounded financial world model for complex markets. Our system brings together events, entities, filings, transcripts, market data, and relationships so LLMs and agents can work from connected context instead of isolated text.
We want to better capture the world and retrieve the right data for agents to execute analysis in a complicated market. That means building a system that understands entities, events, relationships, time, and change well enough to support real reasoning rather than shallow retrieval.
In a complicated market, useful analysis depends on grounded structure. FinCatch gives agents a richer substrate for monitoring, comparison, memory, and explanation so financial reasoning can build on connected market context.
FinGraph is the foundation, but it becomes truly useful when financial context persists, execution stays contained, and workflows are ready to run from day one.
FinCatch uses the idea of a richer knowledge surface for LLMs, but we express it as a financial world model. That means more market data, more temporal structure, more explicit relationships, and a stronger foundation for financial analysis.
FinCatch is built for active financial work. The platform can monitor markets, refresh context, run recurring research tasks, and support forward-looking analysis so users can move from one-off questions to continuous workflows.
FinGraph is the core context layer behind FinCatch. It gives models and workflows a shared financial substrate, so analysis can build on the same entities, events, timelines, relationships, and market links instead of restarting from isolated prompts.
We think financial AI needs a richer context layer: one that understands entities, events, relationships, time, and change well enough to support real reasoning instead of shallow retrieval.
FinGraph is our financial world model: a connected graph of companies, events, relationships, timelines, and market signals that gives agents a durable structure for retrieval, reasoning, and ongoing analysis.
FinCatch is designed for equity research, portfolio work, trading, risk analysis, and adjacent financial workflows that need connected, explainable context instead of one-off black-box answers.
We pair language models with FinGraph, workflows, and market data so analysis can move through graph queries, retrieval, and modeling steps while still feeling like a natural language interface.
We turn search, monitoring, and analysis into ongoing theses for each company, sector, and theme, keeping context, sources, and prior work connected so views can evolve over time rather than restart from scratch.
In the high-stakes world of finance, "trust me, I’m an AI" is not a viable strategy. A single hallucinated statistic or anachronistic data point can trigger million-dollar losses.
Read more →December 7, 2025An exploration of nano-graphrag, a more hackable implementation of GraphRAG for knowledge graph-enhanced retrieval augmented generation
Read more →FinCatch combines an event-centric graph, a multi-agent system, and LLM interfaces into one platform for research, monitoring, and analysis.
