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Open-Source AI Job Hunting Full-Auto Application Manager, a Subscription Service Generating 30k RMB Monthly for Individuals

Workflow: After job seekers submit their basic resume, target industry/position/salary range, the system regularly crawls newly ad

AGENT

Key Fields

FIELD STAMPS

IndustryEducation / Knowledge

RegionChina（中国大陆）

ScaleSME

ChannelOnline

🔧 Workflow

After job seekers submit their basic resume, target industry/position/salary range, the system regularly crawls newly added positions from the previous day on mainstream platforms such as Boss Zhipin, Zhilian Zhaopin, and Liepin. The AI automatically adjusts the core experience description and skill keyword matching degree of the resume based on the position JD, and generates a customized cover letter. Once the preset matching threshold is reached, applications are submitted automatically, and application statuses are recorded simultaneously. Human operators must review the quality of the positions filtered by AI daily, manually reply to communication messages from high-intent HRs, follow up on interview invitations and synchronize them with job seekers, and output job hunting progress reports to clients weekly.

🛠 Setup Requirements

Secondary development is carried out based on GitHub open-source projects such as OfferU, Auto-JobHunter, or career-ops-cn. Requires basic Python skills and browser automation (Playwright/Selenium) rule configuration capabilities. The overall setup period is about 10 to 15 days, including recruitment platform account registration and risk control rule testing, multi-platform position crawling rule adaptation, resume template library building, and matching algorithm parameter tuning. Additional preparation of a residential proxy IP pool, cloud servers, and multi-platform account matrix is required to avoid platform anti-automation bans.

🧰 Toolchain

🔧 OfferU open-source job hunting automation framework
🔧 Playwright browser automation tool
🔧 DeepSeek large language model API
🔧 Boss Zhipin / Liepin recruitment platform account pool
🔧 Residential proxy IP service

💰 Revenue

Adopting a tiered pricing model, standard batch application packages for ordinary positions are priced at 600 to 1200 RMB/person/month, customized matching + application services for mid-to-high-end positions are priced at 1500 to 3000 RMB/person/month, and it can also be charged per interview invitation result at 200 to 500 RMB/time. Serving 25 to 35 clients can achieve a monthly income of 20,000 to 40,000 RMB. If expanding into enterprise campus recruitment batch application services, the unit customer price can be increased to 5,000 to 10,000 RMB/order, offering even higher income potential.

💸 Cost

DeepSeek large model API monthly call fee is about 200 to 500 RMB, residential proxy IP pool monthly fee is about 200 to 400 RMB, 4-core 8G cloud server monthly fee is about 100 to 200 RMB, recruitment platform membership account monthly fee is about 100 to 300 RMB. Total monthly operating cost is about 600 to 1500 RMB, and the profit margin can reach over 60%.

⏱ Time Investment

Invest 2 to 3 hours daily to review AI-filtered job matching results, manually follow up on communication messages from high-intent HRs, and handle client inquiries. Invest 2 to 3 hours weekly to optimize matching algorithm parameters, update the resume template library, and test platform risk control rule adjustments.

🚀 Getting Started

Step 1: Clone the OfferU or Auto-JobHunter open-source project from GitHub, complete local environment setup and basic workflow testing, and run through the entire process of position collection, resume matching, and automatic application using your own resume. Step 2: Publish 3 to 5 free trial slots in fresh graduate job-hunting communities and workplace social platforms, collect feedback, refine the service process, and accumulate 3 to 5 successful cases of getting interview invitations. Step 3: Package successful cases into service promotional materials, launch paid subscription services in job-hunting vertical communities and workplace blogger cooperation channels, and gradually expand the customer scale.

🔑 Keys to Success

✅ Position matching accuracy is the core competitiveness; it is necessary to optimize resume adjustment prompts and keyword matching rules for JDs in different industries to ensure that applied positions highly match the job seeker's background.
✅ Stability management of multi-platform accounts and proxy IPs requires continuously following up on platform risk control rule adjustments and avoiding ban risks through account rotation and application frequency control.
✅ Manual review and follow-up stages are the core of service differentiation. AI is only responsible for batch position filtering and initial application, while humans are responsible for high-intent HR communication, interview progress tracking, and client feedback synchronization.
✅ The tiered pricing model covers different job seeker groups; ordinary position batch applications follow a high-volume low-margin package, while mid-to-high-end management/technical positions follow customized matching high-priced services to increase the overall unit customer price.

⚠️ 风险

⚠️ Upgrades of anti-automation mechanisms by recruitment platforms may cause the system to fail and accounts to be banned, requiring continuous energy investment to follow up on rule adjustments and account pool maintenance.
⚠️ Excessive batch applications may reduce the weight of job seekers' resumes on the HR side or even be marked as spam applications by platforms, requiring strict control over daily application quantities and position matching thresholds.
⚠️ Risk of leakage of job seekers' personal sensitive information and resume data requires encrypted storage of client data and strict setting of access permissions to avoid disputes caused by data leakage.

📌 Real Cases

📌 AI Resume Princess already has over 5,000 managers and middle-to-senior executives paying for its online resume creation, AI optimization, and mock interview services, with annual fees per customer reaching thousands of RMB, validating the willingness of mid-to-high-end job seekers to pay.
📌 The GitHub open-source project Auto-JobHunter has received over 5,000 stars and is used by a large number of individual job seekers and small job-hunting service agencies for automated job applications, validating the feasibility of AI application deployment.
📌 Domestic tool Yijian Zhida focuses on AI fully automated batch application services, having accumulated over 20,000 registered job seeker users, with a monthly retention rate of paid subscription users exceeding 60%.
📌 ClawJob AI Job Hunting Assistant provides full-process services including resume creation, job search, and intelligent application for campus recruitment groups, serving over 10,000 fresh graduates within half a year of launch, with a campus recruitment package payment conversion rate reaching 12%.

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