Most AI tools for private markets are built on large language models — systems designed to retrieve and summarize existing information. Standd takes a fundamentally different approach. Our core technology is a financial world model: a predictive system that builds an internal representation of cause and effect across macro, market, and company-level data, so it can simulate how positions will evolve — not just describe how they've performed. Our predictive models were built with former Department of Defense scientists who specialized in predicting adversarial technological surprise, bringing institutional-grade rigor to a market that has relied too long on backward-looking analysis. Our autonomous agents monitor portfolios for early signs of deterioration, monitor markets and sectors to stay ahead of external pressures, and model growth trajectories, exit multiples, and integration scenarios, giving firms the intelligence to protect value and create it.

Powering this is Exoclaw, the open-source agent framework we developed that is purpose-built for enterprise deployment. We've built them to not just research but build knowledge graphs based on our proprietary framework for building expertise and deepening understand.

Hanno is the combination of autonomous research, compounding knowledge graphs, and a world model that allows humans and agents to simulate and predict before acting. The research below details our methodology, validates our approach against real market data, and shows why predictive intelligence — not faster search — is what private markets need now.