Independent Headhunter AI Sourcing System Outsourcing: Pay-per-Performance to Earn 30K RMB Monthly

💰 Revenue

① Pay-per-interview fee (main revenue): Enterprise clients pay per candidate interview, 500-800 RMB/person. The actual number of interviewees cannot be verified. According to claims, maintaining 3 stable clients can yield a monthly income of 30,000-50,000 RMB, though the share of this line in monthly income is not public (case-based claim, independently unverified); ② Monthly sourcing outsourcing service fee: Enterprises pay a monthly outsourcing fee of 20,000-50,000 RMB/month. The number of contracted clients is unclear, and the total amount collected through this path also has no public figures (case-based claim, no third-party review); ③ Batch campus recruitment sourcing projects: Enterprises pay a one-time service fee per project, 100,000-200,000 RMB/project. How many projects are run in a year is not specified, and the corresponding revenue volume is also uncheckable (case-based claim, no third-party verification); ④ Opportunity item - AI preliminary screening service billed by resume processing volume: Based on a public project test of filtering the most suitable candidates from 1,000 resumes in half an hour as an efficiency reference, billed per resume or monthly subscription. Pricing is not publicly disclosed, and how much can be collected here is only stated by the merchant, independently unverified, and the proportion of revenue is not mentioned.

💸 Cost

GitHub Enterprise subscription $49/month, Hunter.io search quota $29/month, DeepSeek model inference cost about 50-100 RMB/month, n8n self-hosted free of license fees, totaling 500-800 RMB/month.

⏱ Time Investment

Invest 3-4 hours daily, mainly used for manual review of AI-generated poaching letters, handling abnormal account bans, and following up on client feedback. 2 hours can be spent on weekends iterating matching rules and script templates.

🔑 Keys to Success

• ✅ Deep analysis based on open-source code contributions for precise matching, replacing pure keyword matching, with a matching accuracy rate over 30% higher than traditional headhunters
• ✅ Extremely personalized poaching letter content, written by combining the candidate's specific open-source project contribution points, with a response rate 5-10 times higher than templated ones
• ✅ Control sending frequency and platform compliance rules, with no more than 50 messages sent per platform per day to avoid account bans
• ✅ Manual final judgment review of sensitive content to ensure poaching letters comply with labor laws and platform rules, reducing legal risks

⚠️ 风险

• ⚠️ High-frequency scraping of public platform data and batch messaging can easily trigger risk control of platforms like GitHub, LinkedIn, and Boss Zhipin, which can lead to permanent account bans in severe cases
• ⚠️ If AI-generated poaching letters involve salary promises, position exaggeration, and other content, it may trigger disputes between candidates and enterprises, requiring strict manual review
• ⚠️ If violating the Personal Information Protection Law by excessively collecting non-public developer information, administrative penalties may be faced. It must be ensured that all crawled data is publicly accessible content on the platform
• ⚠️ Increased competition leads to downward pressure on service fees, requiring continuous optimization of matching accuracy and script conversion rates to maintain competitiveness