Your investment philosophy,
made systematic.

The story, described

A senior investment team reads deeply but cannot read everything: of roughly a thousand listed large caps, it can follow perhaps fifty, and the rest go unexamined. Screens and quantitative products fill that gap by answering with someone else's philosophy — a hidden model, a house view, a finished list.

ALB measures every company in the universe on 86 quantitative inputs, which are never published. Those roll up into nine analytical families and four investment styles — Growth, Valuation, Quality and Momentum. The manager sets the weight of the four styles; every company's score recomposes, the universe re-ranks into ten deciles where 9 is best, and sector breadth follows. ALB never assigns a sector weight: it inherits one. Only then comes a portfolio: 100 long positions chosen best-in-industry by the engine at its own published weights, each swappable by hand, and a separate short engine of 52 positions. In the app, a manager sets the number of positions and the sectors or sub-industries to exclude, and can run as many portfolio simulations as they like.

The default universe is about a thousand large caps above roughly USD 8 billion — the one ALB's founder uses day to day — and the engine can run on a custom universe for each client. ALB is not built on AI and no AI sits behind its scores; its infrastructure is open, so users can connect their own AI assistant or agents through a dedicated MCP connection to screen the universe, simulate tilts, open a company or export a list.