AI-Powered Investment Research

Discover Quality Stocks
Through Catalyst Research

Portfolio-aware catalyst calendar and long-horizon recommendations powered by agentic workflows, causal intelligence, and machine learning.

How It Works

Three simple steps to transform how you discover and invest in quality stocks.

01

Connect Your Portfolio

Import holdings from brokers or CSV. Define your risk profile, constraints, and investment objectives. Build your watchlist for stocks you're researching.

02

Track Catalysts Before They Hit

AI monitors thousands of sources for upcoming events — earnings, filings, patents, regulatory milestones, supply chain signals. Understand second and third-order impacts through causal analysis.

03

Get Portfolio-Aware Recommendations

Evidence-backed buy/hold/sell suggestions with sizing. Catalyst-driven timing recommendations (not market timing). Clear rationale with uncertainty stated explicitly.

Intelligent Event-Driven Research

Catalystr transforms unstructured information into a portfolio-aware, forward-looking decision layer.

Catalyst Calendar

Track upcoming events for your holdings and watchlist — earnings, filings, regulatory milestones, and more.

Causal Impact Analysis

Understand which stocks are affected by policy changes, supply chain disruptions, or macro events through propagation modeling.

Event Forecasting

Predict the likelihood and timing of catalysts using ML models trained on historical patterns and multi-source signals.

Portfolio Optimization

Get buy/hold/sell recommendations with sizing proposals based on your risk profile, constraints, and objectives.

Real-time Intelligence

Continuous ingestion from news, filings, and specialized databases with instant extraction and scoring.

Evidence-backed Explanations

Every recommendation links to events, sources, and feature contributions with uncertainty clearly communicated.

Ready to Transform Your Research?

Join the waitlist to get early access to Catalystr and start making data-driven investment decisions.

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