Institutional research discipline,
adapted for serious retail investors.
ZISO AI is a pocket research partner and a practical execution coach. It takes over the exhausting market homework and helps investors see the deeper logic behind each decision.
Front-stage service by a research team, back-stage powered by analysis models, quant models, and automated workflow.
Our mission
ZISO AI was built around a direct goal: help ordinary investors operate with institutional-grade research discipline.
Retail investors are usually trapped by fragmented information, time-poor review habits, and reactive decision-making. ZISO AI uses multiple cooperating agents to process daily market inputs, structure the review cycle, and help users leave behind gut-feel trading in favor of calmer, more defensible decisions.
That is the spirit behind the name ZISO. The first part is the deep research work that helps investors see market structure more clearly. The second part is the enduring discipline that protects capital when certainty is weak. Understand the game, but hold the line. That is what makes rational execution possible.
Team and operating structure
We separate research direction, analysis expression, quant engineering, context intelligence, and result auditing into clear roles, then deliver the experience as if a research desk were working alongside the user.
Andre Gu
Leads research direction, systems architecture, and product delivery, turning the quant-plus-AI methodology into a stable, user-facing workflow.
Frank Sun
Owns product strategy, trading framework design, and risk boundaries, ensuring every output remains explainable, actionable, and reviewable.
Produces the lead conclusion, deeper scenario analysis, and core risk judgment, then turns that work into a clear tactical narrative.
Adds supporting analysis and alternate angles, helping translate complex market behavior into judgments that are easier to understand and act on.
Explains the rule-based view, discipline state, and structural constraints, representing the quant rule perspective without pretending to be discretionary judgment.
Builds the quant model foundation, turning data handling, indicators, rules, and parameters into a stable production-grade system.
Filters news and macro noise, then restores the real context around each signal so tactical decisions are not made in a vacuum.
Reviews outcomes after the close, tracks hit rate and model drift, and helps keep the research workflow accountable over time.
Stop trading alone.
Turn on AI-enhanced decision support.
- Mission: Democratize institutional-grade market research for retail investors via a multi-agent AI council.
- Roles: DeepSeek (Tactical reasoning), Hunyuan (Contextual mapping), Quant Engine (Structural rules), Verifier (Outcome auditing).
- Methodology: Separation of concerns ensures that analysis, risk oversight, and historical auditing remain independent and accountable.
Boundary Notice
All content is provided for research and informational purposes only. Nothing on this site constitutes investment advice or a promise of returns.