ZEAI · 体验图谱

ZEAI

Role: Founder / Product Lead

Focus: AI-native content systems · experience modeling · storytelling intelligence

The Question

After years of building large-scale content and creator platforms, I began to notice a fundamental limitation in existing AI systems:

AI can generate content, but it doesn’t truly understand human experience — the lived moments, emotions, context, and meaning that sit beneath stories, images, and language.

The question that led to ZEAI was simple, but deep:

What would an AI platform look like if it could model human experience, not just content?

The Insight

Across platforms like TikTok, Lemon8, and Baidu, I saw the same pattern repeat:

• Content scales, but meaning fragments

• Creators produce, but experience is lost in output

• AI optimizes generation, not understanding

I realized the missing layer wasn’t another model or feature — it was a structured representation of experience itself.

That became the foundation of Experience Graph.

The System: Experience Graph

At ZEAI, I’m building Experience Graph, a system that models human experience as structured, interconnected elements — enabling AI to reason about stories, emotion, and context.

At a high level, the system connects:

• Experiences → lived moments and personal context

• Events → what happened, when, and why

• Emotion & Intent → how it felt and what mattered

• Narrative Structures → how experiences become stories

• Multimodal Expression → language, visuals, and tone

Rather than treating content as isolated outputs, Experience Graph treats it as the surface layer of deeper experiential structure.

Product Exploration

To validate this concept, I’ve been prototyping and experimenting across multiple layers:

• StoryOS

  An AI-native storytelling system that uses experience structures to guide narrative generation rather than prompt-level instructions.

• Little Bridge

  A bilingual (Chinese–English) story creation tool for families and educators, designed as a real-world sandbox to test experience modeling in collaborative storytelling.

• Early Validation Models

  Exploring consumer subscriptions and model-based services to understand how experience-aware AI could be applied across creator tools and content platforms.

Each product is less about scale today, and more about learning how experience can be structured, reused, and understood by AI.

My Role

As Founder and Product Lead, I:

• Defined the core vision and conceptual framework behind Experience Graph

• Designed the system architecture bridging experience, narrative, and multimodal content

• Led early product prototyping and user validation

• Integrated insights from platform-scale systems into an AI-native context

• Continuously refined the system through hands-on experimentation

Why It Matters

ZEAI represents the convergence of everything I’ve worked on so far:

• From UGC and content quality

• To creator lifecycle and platform governance

• To infrastructure and scalable systems

Now applied to a new frontier:

Helping AI understand human experience — not just generate content.

Experience Graph is my attempt to answer a long-term question:

What happens when AI is designed to reason about meaning, memory, and lived experience?

What’s Next

ZEAI is still early — by design.

My focus is on validating the structural correctness of Experience Graph before scaling products, because if the system works, applications will follow naturally.