← Sticker Wall AGENT · DETAIL

AI Mid-to-Senior Executive Customized Resume and Proxy Application Closed-Loop Service - Monthly Revenue of 80,000 RMB

Workflow: Every morning, receive clients' original resumes, target industries, and job descriptions (JDs), call large language mod

AGENT

Key Fields

FIELD STAMPS

IndustryEducation / Knowledge

RegionChina（中国大陆）

ScaleSME

ChannelOnline

🔧 Workflow

Every morning, receive clients' original resumes, target industries, and job descriptions (JDs), call large language models to conduct role-specific customized optimization on resumes, and generate multi-version resumes compliant with Applicant Tracking System (ATS) rules; in the afternoon, run automated application bots to batch-filter positions with over 80% match rates on mainstream recruitment platforms such as BOSS Zhipin and Liepin and complete proxy applications; in the evening, manually verify application results, sync received interview invitations to clients, and collect interview feedback to iterate on the resume matching model.

🛠 Setup Requirements

Requires basic proficiency in Python programming and browser automation technologies like Playwright, local deployment of open-source job-hunting automation frameworks, preparing 3-5 recruitment platform accounts and a dynamic IP proxy pool initially to handle platform risk control. Running through the entire process and testing takes only 2-3 weeks of spare time, with no complex server configuration required.

🧰 Toolchain

🔧 Auto-JobHunter open-source framework
🔧 Yijian Zhida software
🔧 AI mock interview API
🔧 Liepin Enterprise Edition account

💰 Revenue

① Mid-to-senior executive package service fee (main revenue): job seekers with annual salaries of 300,000 RMB and above pay monthly, with individual packages priced at 3,000-5,000 RMB/month. Serving 20 clients steadily per month yields a monthly revenue of 80,000 RMB (exact proportion of total monthly revenue unspecified; self-reported figure by the case study, independently unverified as of 2026); ② Pay-per-use for batch proxy applications: targeting positions like AI product managers with salaries of 20-30k for directional scraping and application. Operation panels show a single person applying to 8 jobs per week, but unit prices for proxy applications and monthly application volumes have no public data, and the revenue share is unknown (also provided by the case study party); ③ Interview guarantee performance-based commission: adding a performance-based agreement can elevate the average customer unit price to over 8,000 RMB, with commissions drawn based on results. Commission percentages and the number of qualifying individuals are not disclosed, so the contributed share cannot be listed separately (similarly based on the case party's statement, independently unverified); ④ Opportunity item - Enterprise recruitment subscription: charging enterprises a subscription fee per seat, with no current basis for seat pricing or the number of signed enterprises.

💸 Cost

The major expenses consist of three parts: around 300 RMB per month for large language model APIs, approximately 200 RMB per month amortized for enterprise account annual fees on recruitment platforms, and 500 RMB per month for the dynamic IP proxy pool. Total monthly expenses are kept under 1,500 RMB, with almost no additional cost incurred for taking on more orders in the future.

⏱ Time Investment

Dedicate about 3 hours per day, including 1 hour for handling client requirements and resume iteration, 1 hour for configuring application tasks, and 1 hour for manually verifying application results and following up on interview invitations.

🚀 Getting Started

Step 1: Download open-source frameworks like Auto-JobHunter for local deployment, run through the entire 'resume generation - job matching - batch application' process using your own test resume, and accumulate over 10 genuine interview invitation samples; Step 2: Publish content related to 'mid-to-senior executive job hunting companion coaching' on platforms like Xiaohongshu and Zhihu, using authentic interview invitation screenshots as trust endorsements to guide traffic into private domains for conversion; Step 3: Connect with headhunting channels to undertake overflow mid-to-high-end job demands from enterprises and improve matching efficiency.

🔑 Keys to Success

✅ Precise job matching algorithms serve as the core barrier, requiring continuous iteration of ATS adaptation rules
✅ Human mentors strictly control resume authenticity to eliminate background check risks caused by over-packaging
✅ A commercial model featuring tiered pricing based on the number of interview invitations, resulting in extremely high client willingness to pay and high repeat purchase rates
✅ Dynamic account pools and anti-ban strategies serve as the critical foundation for stably executing the entire workflow

⚠️ 风险

⚠️ Excessively high automated application frequencies easily trigger risk control mechanisms on recruitment platforms like BOSS Zhipin and Liepin, leading to account bans
⚠️ Over-optimizing candidate resumes may trigger compliance risks in corporate background checks or even lead to complaints
⚠️ Policy adjustments by recruitment platforms may restrict automated application functions, requiring continuous tracking of platform rules to iterate technical solutions

📌 Real Cases

📌 Individual developer @Programmer Qiezi built the Career-Ops job-hunting automation system based on Claude Code, achieving monthly revenue of over 30,000 RMB through proxy application services
📌 The open-source project Auto-JobHunter gained 1,200+ stars on GitHub, with over 200 individual practitioners carrying out job-hunting proxy application services based on the framework
📌 Domestic job service platform 'Yijian Zhida' has cumulatively helped over 10,000 job seekers complete AI customized resumes and batch applications, raising the average resume response rate to 18%

Sources