AdPilot

AI Agent for Cross-Border Ad Management

简体中文

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AdPilot is an AI Agent that helps cross-border e-commerce retailers and international trade enterprises manage their advertising operations through natural language interaction. Instead of navigating complex ad platform UIs across slow cross-border networks, users simply tell AdPilot what they need — and the Agent handles the rest through a modular skill system.

Why AdPilot?

Cross-border advertisers face a unique combination of pain points that generic ad tools don't address:

• Slow cross-border networks — Ad platform UIs load megabytes of HTML/CSS/JS and redundant derived metrics. During peak hours, even checking basic ad performance becomes unbearable.
• Complex multi-step operations — Creating a campaign requires navigating 10+ screens with dozens of configuration options.
• Time zone gaps — Ads underperform for hours while the team sleeps on the other side of the world.
• Scattered knowledge — Optimization best practices live in team members' heads, not in systems.

AdPilot solves these by putting an AI Agent between users and ad platforms. The Agent executes operations through lean, cached API calls — no bloated UI, no redundant data, no wasted bandwidth.

Architecture

graph TB
    User["👤 User"] -->|natural language| Agent["🤖 AdPilot Agent"]
    
    Agent --> SK["📦 Skill System"]
    
    subgraph Skills["Agent Skills"]
        S1["🛒 Account Acquisition"]
        S2["📝 Ad Creation"]
        S3["🎛️ Ad Management"]
        S4["📊 Data & Analytics"]
        S5["🤖 ML Prediction"]
        S6["⚙️ Rule Automation"]
        S7["📋 Reporting"]
    end
    
    SK --> Skills
    
    subgraph DataLayer["⚡ Smart Data Layer"]
        Cache["Request Cache<br/>Clean API, no bloat"]
        Sync["Incremental Sync<br/>Off-peak scheduling"]
        Derive["Local Derivation<br/>ROAS, CPC from base metrics"]
    end
    
    Skills --> DataLayer
    DataLayer --> API["Ad Platform APIs<br/>Facebook · Google · Pinterest"]

Skills Overview

Skill	Description
🛒 Account Acquisition	Guide users through ad account purchase and setup
📝 Ad Creation	Create campaigns, ad sets, and ads via natural language
🎛️ Ad Management	Start/stop ads, adjust budgets, modify targeting
📊 Data & Analytics	Query performance data with intelligent caching
🤖 ML Prediction	ROAS prediction and anomaly detection
⚙️ Rule Automation	Configure automated rules in natural language
📋 Reporting	Generate performance reports and recommendations

Documentation

• Project Specs · 项目提案
• Architecture · 系统架构
• Evaluation Criteria · 评测标准
• User Scenarios · 用户场景
• Roadmap · 开发路线图

Competition

• Event: OPC 2026 AI Hackathon
• Track: 27 — AI + Retail / AI + Trade
• Author: lprintf

License

MIT