AI-native financial research and analysis platform for individual equity investors and small hedge funds
A system of record that replaces spreadsheets, PDFs, and manual data gathering for equity research by ingesting filings, earnings calls, and market data into a unified research ontology, then allowing agents to run analysis queries, generate comparables, and produce investment theses end-to-end.
The problem
Individual investors and small hedge funds conduct equity research across 12+ browser tabs, manual spreadsheet models, and scattered PDFs. They spend hours extracting data from SEC filings, cross-referencing earnings transcripts, building comps, and organizing analysis—work that is repetitive, error-prone, and impossible to audit or reproduce.
Who has it: Solo equity analysts, small RIA teams (2–10 people), and emerging hedge funds managing $50M–$500M in AUM who conduct deep fundamental research and lack the capital to subscribe to Bloomberg, FactSet, or hire dedicated research staff.
Why now: LLMs can now reliably extract structured data from unstructured filings and earnings calls; agents can coordinate multi-step research workflows; and open-access filings APIs and earnings-transcript databases are mature. The friction of manual research is now a clear target for AI automation.
Where this came from
2 public sources behind this idea.
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