AI-native Investing

Solvect AI is an AI-native quantitative finance system designed to run the full investment cycle—from opportunity discovery and decision-making to execution and learning from outcomes. It uses a real-time chat-to-graph architecture and an Event → Asset → Action → Feedback multi-agent loop to transform unstructured market signals into structured reasoning graphs and executable actions.

I initiated and led the project from concept to MVP in collaboration with an AI engineer, owning product definition, interaction design, and front-end development. I defined how users and AI agents interact, shaped agent behaviors and system architecture, and designed interfaces that keep reasoning, causality, and control explicit in a high-stakes financial context.

We shipped an MVP within three months, and our open-source version (excluding design assets) has gained strong traction on GitHub.