See the supply chain dependencies the market hasn’t priced in yet.

Knowledge Engine

A portfolio manager does not need more transcripts. A portfolio manager needs a system that converts primary disclosures into investable structure. The first layer is a Knowledge Engine: LLMs extract supply chain facts, management signals, and relationship changes from filings and earnings calls, while a consensus process admits only information that independently verifies against the same source. Agents then keep that knowledge current through daily monitoring, so the map does not decay between quarters.

Signal Engine

The second layer is a Signal Engine. Once the knowledge is structured as a living company graph, models search for patterns that matter to positioning: where pressure is building, where demand is propagating, and which changes are likely to transmit across names before they show up in consensus revisions. Supervised learning calibrates signal relevance against historical outcomes; reinforcement learning optimizes the ranking policy so that top-of-feed signals consistently map to actionable PM decisions. Signals are continuously scored, decayed, and re-ranked — the system learns what matters, not just what changed.