UU Paotui leveraged Qoder's Wiki + Rules + Skills to transform AI from an "outsider" into a full-lifecycle teammate.
Hi everyone, I'm Yuan Zhao, Head of Engineering at UU Paotui. Today I'll share how we embedded AI into every stage of our development workflow.
As a leading on-demand delivery platform, we process millions of orders daily. Our tech stack is a typical microservices architecture with over 800 core services, spanning frontend, backend, and big data. Balancing development velocity with code quality in such a massive, complex system has always been our core challenge.
As AI coding tools became widespread, new problems emerged: AI-generated code was fast but stylistically inconsistent, actually increasing code review overhead. More critically, AI didn't understand our complex business logic or microservices architecture—generated code often required heavy modifications before it was usable.
We identified four core pain points:
Facing these pain points, our core philosophy shifted. We stopped treating AI as merely a coding assistant and committed to making it a first-class participant in our development process. This meant AI needed to understand our business context, embed standards into its generation process, retrieve our accumulated knowledge, and ultimately unify design, coding, and review into a single intelligent flow.
Our solution: restructure the entire development workflow on the Qoder platform. We built a knowledge foundation centered on enterprise-level Repo Wiki, then layered on a standards tier (Rules), a design tier, and a quality tier (Skills). With this architecture, AI learns our knowledge, standards, and design patterns before generating a single line of code—ensuring quality and business alignment from the source.
The goal: every line of AI-generated code complies with our enterprise coding standards.
We distilled our standards, encoded them as Qoder Rules, and integrated them into the AI generation process—achieving "compliant by default."
Result: first-pass compliance rate jumped from 60% to 95%.
We designed an intelligent Code Review Skill in Qoder. It can be triggered via Merge Request or conversation, reviewing code across multiple dimensions—coding standards, potential bugs, performance, and security—then outputting a detailed report. This dramatically reduced the manual review burden.
Through standardized tagging and automated pipelines, engineers are freed from tedious release operations to focus on delivering business value, while ensuring release safety and consistency.
The fundamental problem: AI didn't understand our complex architecture. Our approach—first teach AI the system, then let it participate in design.
Step one was knowledge capture. Using Qoder, we parsed existing microservices and auto-generated application-level Wiki documentation. With Qoder Quest Mode, the complete development cycle for a feature takes only 3 hours: 1.5 hours writing specs, 30 minutes of AI generation, minutes-level Code Review, and 30 minutes of code refinement. A massive efficiency gain.
We also exposed APIs as MCP endpoints covering our city-level backend, building a standardized interaction hub between AI Agents and business systems.
In the traditional workflow, AI only provided fragmented assistance during coding. In our new workflow, AI is deeply embedded in design, generation, and review—every stage built on our knowledge foundation and standards. The efficiency leap is qualitative.
Key insight: we don't manually write complex documentation anymore. All Wiki, Rules, and Skills are AI-generated. As long as we feed in the right enterprise context, AI outputs exactly what we need.
Looking ahead, we see enormous untapped potential. We're exploring:
I. Why UU Paotui Needed AI Across the Full Development Lifecycle
Technical Background
As a leading on-demand delivery platform, we process millions of orders daily. Our tech stack is a typical microservices architecture with over 800 core services, spanning frontend, backend, and big data. Balancing development velocity with code quality in such a massive, complex system has always been our core challenge.
As AI coding tools became widespread, new problems emerged: AI-generated code was fast but stylistically inconsistent, actually increasing code review overhead. More critically, AI didn't understand our complex business logic or microservices architecture—generated code often required heavy modifications before it was usable.
We identified four core pain points:
- Knowledge lived in people's heads—impossible to effectively preserve or transfer.
- Our coding standards existed only on paper—enforcement was inconsistent.
- Design patterns couldn't be reused—resulting in redundant work.
- AI tools felt more like an "outsider" that constantly needed correction, rather than a trusted teammate.
II. A New Paradigm: From "AI-Assisted Coding" to "AI-Embedded Workflow"
Facing these pain points, our core philosophy shifted. We stopped treating AI as merely a coding assistant and committed to making it a first-class participant in our development process. This meant AI needed to understand our business context, embed standards into its generation process, retrieve our accumulated knowledge, and ultimately unify design, coding, and review into a single intelligent flow.
Our solution: restructure the entire development workflow on the Qoder platform. We built a knowledge foundation centered on enterprise-level Repo Wiki, then layered on a standards tier (Rules), a design tier, and a quality tier (Skills). With this architecture, AI learns our knowledge, standards, and design patterns before generating a single line of code—ensuring quality and business alignment from the source.
III. Workflow Transformation in Practice
Practice 1: Teaching AI to "Speak Our Language"
The goal: every line of AI-generated code complies with our enterprise coding standards.
We distilled our standards, encoded them as Qoder Rules, and integrated them into the AI generation process—achieving "compliant by default."
Result: first-pass compliance rate jumped from 60% to 95%.
Practice 2: AI-Powered Code Review
We designed an intelligent Code Review Skill in Qoder. It can be triggered via Merge Request or conversation, reviewing code across multiple dimensions—coding standards, potential bugs, performance, and security—then outputting a detailed report. This dramatically reduced the manual review burden.
Through standardized tagging and automated pipelines, engineers are freed from tedious release operations to focus on delivering business value, while ensuring release safety and consistency.
Practice 3: Giving AI Full System Awareness
The fundamental problem: AI didn't understand our complex architecture. Our approach—first teach AI the system, then let it participate in design.
Step one was knowledge capture. Using Qoder, we parsed existing microservices and auto-generated application-level Wiki documentation. With Qoder Quest Mode, the complete development cycle for a feature takes only 3 hours: 1.5 hours writing specs, 30 minutes of AI generation, minutes-level Code Review, and 30 minutes of code refinement. A massive efficiency gain.
We also exposed APIs as MCP endpoints covering our city-level backend, building a standardized interaction hub between AI Agents and business systems.
In the traditional workflow, AI only provided fragmented assistance during coding. In our new workflow, AI is deeply embedded in design, generation, and review—every stage built on our knowledge foundation and standards. The efficiency leap is qualitative.
Key insight: we don't manually write complex documentation anymore. All Wiki, Rules, and Skills are AI-generated. As long as we feed in the right enterprise context, AI outputs exactly what we need.
- Project Kickoff: Import PRD into the knowledge base, generate high-level design.
- Detailed Design: Generate detailed specs based on the high-level design and existing code.
- Coding: Develop under Rule constraints.
- Review: Automated Code Review via Skills.
- Testing & Release: AI generates unit tests and functional test cases (70%-80% adoption rate), then one-command release via CLI.
IV. The Future of This New Paradigm
Looking ahead, we see enormous untapped potential. We're exploring:
- Intelligent Testing: Beyond unit and functional tests, focusing on UI automation and business-log-based vulnerability diagnosis.
- MCP Scenarios: Packaging admin capabilities into standardized interfaces and MCP endpoints—democratizing development so business teams can build custom plugins to boost personal productivity.
- End-to-End Automation: As model capabilities advance, further optimizing workflows toward true end-to-end intelligent delivery.