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Review Auto-Reply White-Label API · Monthly Income of 13,000 RMB

Workflow: Every morning, the system automatically pulls all new platform reviews added by connected merchant stores on the previou

AGENT

Key Fields

FIELD STAMPS

IndustryLocal Services

RegionChina（中国大陆）

ScaleSME

ChannelOnline

🔧 Workflow

Every morning, the system automatically pulls all new platform reviews added by connected merchant stores on the previous day. It currently supports multi-platform interface integration including Meituan, Dianping, Amap (Gaode), and Google. First, it uses a sentiment analysis model to determine review star ratings, identify core demand keywords in negative reviews, and filter out malicious reviews and advertising content. For positive reviews, it automatically generates a thank-you reply with the merchant's brand tone; for neutral and negative reviews, it flags core issues and generates a differentiated soothing reply draft, pushing it to the agency's review backend. After manual confirmation that the content complies with regulations and meets the merchant's brand requirements, it is sent back to the corresponding platform with one click. At the end of each month, it automatically aggregates data such as changes in the positive review rate throughout the cycle, categorization of high-frequency negative review issues, peer store rating comparisons in the same city, and reply conversion data. It then automatically generates a customer relationship analysis monthly PDF report featuring the agency's brand logo, which can be directly delivered to merchant clients for business reviews.

🛠 Setup Requirements

If you have basic API development skills, you can use Python FastAPI to build a multi-tenant white-label interface. If you don't know code, you can use n8n low-code tools to build the core workflow, eliminating the need to write backend code from scratch. You need to apply for developer permissions for the review data interfaces of various platforms. Some platforms open public review scraping interfaces, which can be operated within compliance without paying high interface fees. Configure the API keys of OpenAI or domestic compliant large models (such as Ernie Bot, Qwen) for reply generation and sentiment analysis. Build a merchant-level data-isolated PostgreSQL database to store review history, reply tone preferences, and brand tone parameters for each store, ensuring that data from different merchants is not leaked. It is estimated that building the initial version takes 1 week with no-code, or 3-5 days to run through the core chain with basic development skills. Afterwards, prompt templates need to be continuously iterated based on merchant feedback to adapt to the reply styles of different industries.

🧰 Toolchain

🔧 n8n
🔧 OpenAI API
🔧 PostgreSQL
🔧 Feishu Bot
🔧 Platform Review Open APIs

💰 Revenue

Referring to the public pricing of overseas peer tool RevioReputation at $59 per store per month, the monthly fee for domestic peer agency review hosting services generally ranges between 300-800 RMB per store. If the white-label API model is used to charge agency companies with a commission of 50-100 RMB per store per month, signing 30 stores can yield a monthly income of 1,500-3,000 RMB. If customized customer relationship monthly report service fees are added, it can generate an additional 100-200 RMB per merchant per month, stably pushing total monthly income past 10,000 RMB. Expanding to 100 cooperative stores can yield a monthly income of over 8,000 RMB. If charged per API call volume, income will grow linearly with the number of stores covered by the white-label partner, as top agencies can have thousands of stores under their umbrella, pushing annual income past 100,000 RMB.

💸 Cost

A lightweight cloud server is sufficient to meet server requirements, with a monthly cost of about 20-30 RMB. Large model API costs are about 50-80 RMB per month (estimated based on a call volume of 100 reviews per day for 30 stores; the domestic large model API call cost has dropped to less than 1 RMB per 1,000 tokens), bringing the total basic monthly cost to no more than 120 RMB. If connecting to the official paid interfaces of certain platforms, the call cost per review is about 0.01 RMB, requiring only about 90 RMB more per month for 30 stores, keeping the total cost under 200 RMB per month with extremely low marginal costs.

⏱ Time Investment

Investing about 1 hour per day, mainly for inspecting the operation status of interfaces, processing abnormal reviews flagged by the system (such as malicious negative reviews or rule-breaking content containing sensitive words), and reviewing AI-generated reply drafts for content inconsistent with the merchant's brand tone. There is no need for line-by-line modifications, as less than 10% of abnormal content requires handling. Investing 2 hours at the end of each month to adjust customer monthly report templates, check data statistics criteria, and follow up on merchant renewal needs. The overall time investment is extremely low, making it suitable for individuals or small teams to operate part-time without taking up too much primary business time.

🚀 Getting Started

Step 1: Join WeChat and Douyin merchant communities, and Zhihu local lifestyle circles related to local lifestyle agency operations, Meituan merchant services, and Dianping merchant operations. Filter out agency companies or chain catering and beauty merchants that have more than 3 stores and are struggling with review replies. Propose a 1-month free trial service, requiring only store review data interface permissions from the other party with zero upfront fees, in exchange for cooperative opportunities for subsequent charging. Step 2: Collect at least 100 authentic historical reviews from merchants, and train exclusive reply script templates combined with the merchant's brand tone. After running through the core workflow, quote prices to merchants using reply effectiveness cases generated during the free trial period (such as specific data on improved negative review response timeliness and rising store ratings). Pricing the monthly fee at 300-500 RMB per store allows for rapid scaling. Focus on word-of-mouth cases in the early stage, and rapidly snowball after accumulating 3-5 successful clients.

🔑 Keys to Success

✅ Compliance and compatibility capabilities for multi-platform review interfaces, covering mainstream domestic local lifestyle platforms such as Meituan, Dianping, and Amap. Confirm interface compliance requirements with platforms in advance to avoid service interruptions caused by non-compliant scraping.
✅ A mandatory manual review pre-check stage must be set up. All AI-generated reply drafts must be confirmed by the merchant or agency before publishing to prevent merchant accounts from being banned due to rule-breaking content or false advertising.
✅ Customer monthly reports must include core data that merchants genuinely care about, such as root cause analysis of negative reviews, peer rating comparisons in the same city, reply effectiveness statistics, and business suggestions, rather than simple data listings, which significantly boosts client retention rates.
✅ Customize exclusive reply script templates tailored to merchants in different industries and brand tones. For example, catering should be friendly and approachable, while high-end beauty should be professional and polite. A single template cannot be universal; ensure reply content matches the merchant's brand positioning.

⚠️ 风险

⚠️ Local lifestyle platforms may tighten policies on third-party auto-reply interfaces. If platforms restrict auto-reply functions, a compliant manual-assisted review + semi-automated reply scheme must be deployed in advance to prevent core functions from failing.
⚠️ If agency companies independently develop similar tools, it may lead to the loss of white-label clients. Clients must be bound through value-added services such as continuously optimizing reply accuracy, providing customized monthly reports, and dedicated operational consultants to increase switching costs.
⚠️ If auto-generated replies involve false advertising or violate platform rules, they may jointly lead to merchants' accounts having traffic restricted or even being banned. Strict sensitive word filtering and content review mechanisms must be established to intercept rule-breaking scripts in advance while clarifying responsibility boundaries with merchants.

📌 Real Cases

📌 Wenmai AI Browser launched a seller review reply assistant, which has served thousands of e-commerce merchants. It automatically adapts to review rules across platforms like Taobao, JD.com, and Pinduoduo for batch replies, saving each merchant about 2,000 RMB in manual operation costs per month and increasing reply efficiency by 80%.
📌 Shuoku Octopus RPA provides an AI intelligent reply robot covering mainstream e-commerce and local lifestyle platforms, supporting customized reply script templates. It increases review reply efficiency by an average of 80% per client per month and shortens negative review response timeliness to within 2 hours.
📌 After a chain restaurant brand in Hangzhou connected to a customized AI review response system to automatically reply to new reviews on Meituan and Dianping, and monthly generate customer relationship analysis reports with business suggestions, the average negative review response timeliness shortened from 26 hours to 1.5 hours. After 3 months, the average store rating increased from 4.1 to 4.6, and the merchant retention rate remained at 100%.

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