Give Portfolio Managers Investment Memory That Tracks Every Thesis

You asked the AI about a 10-Q surprise. It analyzes the document — but doesn't know you've held the name three years, what the original thesis was, or which earlier red flags you watched. Without memory, AI research output is a shallow read on each new prompt.

Per-name memory — Each ticker has its own memory namespace: thesis, holdings history, analyst notes, conviction changes.

Built-in SEC EDGAR + financial data — 3M+ SEC filings, real-time stock/crypto/FX, World Bank, FRED — instantly queryable.

Reflection memory for thesis evolution — How your conviction has changed, what triggered each change, what would force a reversal.

Event memory for catalyst tracking — Earnings, management changes, regulatory events — chronological per name.

1. Connect — Ingest analyst notes, IC memos, filings, and prior call transcripts.
2. Structure — Each becomes typed memory: thesis (background), filings (facts), earnings calls (events).
3. Reuse — Before any AI-assisted research turn, the relevant per-name memory loads automatically.

Hedge funds, family offices, and asset managers using AI in research workflows — where conviction depends on years of accumulated context per holding and the cost of losing that context to AI amnesia is real performance.