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AI Batch Job Matching & Application Monthly Subscription Service - 15,000 RMB/Month · High-Conversion Application System

Workflow: Daily input of the candidate's educational background, work experience, target industry, and job keywords. The system fi

AGENT

Key Fields

FIELD STAMPS

IndustryEducation / Knowledge

RegionChina（中国大陆）

ScaleSME

ChannelOnline

🔧 Workflow

Daily input of the candidate's educational background, work experience, target industry, and job keywords. The system first calls the large model API to parse the candidate's resume in a structured manner, then uses open-source application scripts to batch scrape newly posted jobs of the day from mainstream recruitment platforms such as BOSS Zhipin, Liepin, and 51job, and scores and sorts the jobs based on the JD keyword matching degree. Subsequently, 3-5 differentiated resume copies are automatically generated, giving priority to applying for positions with a matching degree of 80 or above. In the evening, manual review of application feedback is conducted, candidate information who received interview invitations is synchronized with users, and resume keyword libraries and application strategies are iterated based on interview feedback, forming a closed-loop optimization.

🛠 Setup Requirements

A cloud server is required to run open-source batch application scripts (such as OfferU, JobsIn, Auto-JobHunter). A large model API interface must be enabled for resume parsing and job matching. Register 3-5 enterprise HR/job seeker accounts on mainstream recruitment platforms as application vehicles, and build a proxy IP pool to bypass platform anti-risk controls. Technically, only basic script deployment and debugging skills are required. Beginners can master the single-platform application process within 1-2 weeks. The core threshold lies in bypassing platform rules and refining application conversion rates, with an overall setup cycle of about 7-10 days.

🧰 Toolchain

🔧 OfferU
🔧 JobsIn
🔧 Auto-JobHunter
🔧 Zhipu Qingyan API
🔧 Alibaba Cloud Server

💰 Revenue

Adopts a monthly subscription pricing model. The average order value for regular social job seekers is 299 RMB/month, and for campus/mid-to-high-end customized applications, it is 799 RMB/month. Maintaining 50 regular paying clients + 10 mid-to-high-end paying clients yields a stable monthly revenue of about 23,000 RMB. If clients repurchase or refer others, the income can gradually increase to over 30,000 RMB/month, making it a stable cash flow model with compounding potential.

💸 Cost

Monthly fixed costs are about 800 RMB: large model API call fees (resume parsing, job matching, resume generation) are about 300 RMB/month; a 2-core 4G configuration cloud server is about 200 RMB/month; proxy IP pool and multi-account mobile phone number monthly rentals are about 200 RMB/month; platform CAPTCHA recognition service is about 100 RMB/month.

⏱ Time Investment

Invest 3-4 hours daily, including 1 hour for reviewing application feedback and handling user inquiries, and 2-3 hours for iterating resume templates and adjusting application strategies. Weekends can be used to intensively optimize service processes and customer acquisition channels.

🚀 Getting Started

Step 1 for beginners: Download the open-source application project OfferU or JobsIn from GitHub, run the automated application process for a single platform after local deployment, and test the pass rate and response rate of applying to 100 jobs; Step 2: Provide free application services for 5-10 job-seeking friends around you, and polish at least 3 real-world cases of successfully securing interview invitations; Step 3: Publish service information in university employment communities and workplace vertical communities, launch a first-month promotional price to attract the first batch of paying users, and gradually raise prices after accumulating word-of-mouth reputation.

🔑 Keys to Success

✅ Dynamically adjust resume copies strictly according to the job JD. Do not apply for positions with a matching degree lower than 80 to avoid invalid applications wasting account weight and to improve the initial screening pass rate.
✅ Build a multi-account rotation and proxy IP pool mechanism, strictly control the daily application volume of a single account to be below the platform threshold, and regularly update account statuses to avoid ban risks.
✅ Prioritize displaying real success cases (including interview invitation screenshots and offer screenshots) to build user trust, clearly inform users of the service effect boundaries, and avoid over-promising to prevent disputes.
✅ Establish a user feedback closed loop. After each application, iterate the resume keyword library and application rules based on platform responses to gradually improve matching accuracy and user retention.

⚠️ 风险

⚠️ Upgrades to recruitment platform anti-cheat mechanisms could cause batch application scripts to fail or lead to large-scale account bans, resulting in service interruptions and user refunds.
⚠️ Candidate resume information contains sensitive personal data; leakage during storage or transmission may trigger compliance risks and legal disputes.
⚠️ Recruitment platforms adjusting job scraping rules or application frequency limits could lead to a decline in job matching accuracy and reduced user willingness to pay.
⚠️ Over-promising interview pass rates or offer acquisition rates could lead to user complaints and refunds, severely damaging reputation and long-term operations.

📌 Real Cases

📌 The open-source project OfferU has gained 1200+ stars on GitHub and has been forked and used by over 2000 developers, verifying the feasibility of batch application scripts across multiple platforms.
📌 The official website of AI Resume Ji disclosed that over 5,000 managers and mid-to-high-level executives have paid to use it, with single order values covering multiple tiers from 99 RMB to 999 RMB, and annual revenue exceeding 10 million RMB.
📌 Case records of the GitHub project Auto-JobHunter show that an individual developer used this script combined with AI resume optimization services to serve a total of 120 job seekers within 3 months, with monthly revenue stabilizing above 18,000 RMB.
📌 The official website of Yijian Zhida promotes that its AI batch application system has served over 20,000 job seekers, with a monthly paid conversion rate of 18%, and users receiving interview invitations within an average of 7 days after applying.

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