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Xianyu Negative Review Recovery AI Co-pilot Subscription: Monthly model helps individual sellers maintain star ratings with monthly revenue of 12,000 RMB

Workflow: The input consists of newly scraped negative reviews of the store each day; the processing layer uses Coze or n8n to orc

AGENT

Key Fields

FIELD STAMPS

IndustryE-commerce / Retail

RegionChina

ScaleSME

ChannelOnline

🔧 Workflow

The input consists of newly scraped negative reviews of the store each day; the processing layer uses Coze or n8n to orchestrate Qwen to generate scripts with fixed rules: negative reviews are categorized into four types (logistics, quality, description mismatch, and malicious), compensation vouchers are tiered (under 200 RMB: 5 RMB, 200-800 RMB: 15 RMB, over 800 RMB: 30 RMB), and bargaining follows a three-round concession of 3% -> 5% -> 7%. The output is a summary table of pending scripts and compensations, completed within 30 minutes from the appearance of the negative review to the target push; the frequency is 2-3 time slots per day, and messages are sent with a one-click confirmation by the seller before dispatch (merchant case data has not been independently verified).

🛠 Setup Requirements

Requires basic Python skills, the ability to run open-source projects like xianyu-ai-agent on GitHub, and configuring Coze workflows to connect to DeepSeek or Qwen APIs; uses cloud functions for scheduled triggers, taking 1 to 2 weeks to build; no development team is needed—just one person and a computer.

🧰 Toolchain

🔧 GitHub YunhaoDou/xianyu-ai-agent open-source Xianyu trading assistant agent
🔧 Coze negative review analysis and script workflow
🔧 DeepSeek or Qwen large model API
🔧 n8n automation orchestration
🔧 5118 negative review conversion assistant

💰 Revenue

① Professional seller monthly subscription (primary revenue): Sellers subscribe and pay monthly, 199 RMB/month x 30 stores approx. 6,000 RMB/month, scaling to 60 stores approx. 12,000 RMB/month. Almost all monthly revenue relies on this channel (approx. 100% share), calculated by multiplying the monthly fee by the number of sellers, observed in 2026; ② Per-order commission recovery: Sellers pay per successful recovery order, referencing similar models at 18,000 RMB per month (exact per-order fee not publicly disclosed, share not specified—merchant self-reported data, unverified independently); ③ Compensation calculation and auto-follow-up: Sellers pay via subscription or per-use, orders < 200 RMB compensated 5 RMB, 200-800 RMB compensated 15 RMB, > 800 RMB compensated 30 RMB, response completed within 30 minutes, stress test accuracy 92.7%, unit price and market share not disclosed (case self-reported data, without independent verification); ④ Opportunity item - Cross-platform customer service efficiency tool resale: Priced via seat subscription, benchmarked against 30%+ improvement in customer service efficiency and recognition from 50,000+ customers, exact revenue figures not yet available.

💸 Cost

Core tools are mostly open-source or free, requiring only pay-as-you-go LLM API fees of approximately 50 to 200 RMB per month; adding Coze paid workflows or 5118 negative review conversion assistant subscriptions adds under 100 RMB per month.

⏱ Time Investment

About 2 to 3 hours per day, mainly for new negative review confirmation and complex manual buyer follow-ups, with the rest handled automatically by the agent.

🚀 Getting Started

First, go to GitHub to run the YunhaoDou/xianyu-ai-agent project and understand its complete workflow of scraping, replying, and following up; then find 5 to 10 professional sellers on Xianyu willing to try it for a free one-month service to accumulate real negative review data and script samples; price and charge only after achieving results.

🔑 Keys to Success

✅ Scripts should simulate human customer service tone and incorporate emotional care, as mechanical AI replies easily trigger secondary dissatisfaction from buyers
✅ Compensation plans should be calculated based on product unit price and positive review weight, prioritizing low-cost, high-perception solutions such as small refunds or complimentary gifts
✅ Negative review classification must precisely distinguish logistics issues, quality problems, description mismatches, and malicious reviews to avoid misapplying compensation rules
✅ Fix 2-3 specific time slots per day to handle negative review communication, preventing buyers from waiting too long and closing the negotiation window

⚠️ 风险

⚠️ The Xianyu platform has strict risk control on automated replies; frequent AI conversations can easily trigger traffic limits or account bans, requiring manual confirmation steps
⚠️ If the AI-generated compensation plan exceeds the merchant's cost budget, it may cause merchants to terminate cooperation and reduce customer retention rates
⚠️ Some buyers can identify AI communication traits, creating a sense of perfunctoriness that intensifies conflicts and escalates into platform complaints

📌 Real Cases

📌 GitHub open-source Xianyu AI trading assistant agent, providing a complete reference implementation for negative review scraping, auto-reply, and negotiation follow-up. It is a common technical foundation for individual agency operation services, with over 1.2k stars.
📌 An automated negative review recovery tool targeting e-commerce scenarios, serving over 20,000 sellers. It supports one-click output for negative review classification, script generation, and compensation calculation, charging per successfully recovered order. Top service providers recover over 100,000 negative reviews per month.
📌 A multi-agent collaborative intelligent customer service system supporting platforms like Taobao, Pinduoduo, and Douyin for automated negative review identification and compensation plan generation. After integration, merchants saw a 32% increase in negative review appeal success rates and a 45% reduction in manual customer service costs.

Sources