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AI Headhunting Candidate Panoramic Report Outsourcing: Automatically Generate Candidate Matching Summaries for a Monthly Income of 15,000 RMB

Workflow: Every day, through public APIs or compliant crawlers of platforms such as GitHub, LinkedIn, BOSS Zhipin, and Liepin, pub

AGENT

Key Fields

FIELD STAMPS

IndustryFintech

RegionChina

ScaleSME

ChannelOnline

🔧 Workflow

Every day, through public APIs or compliant crawlers of platforms such as GitHub, LinkedIn, BOSS Zhipin, and Liepin, public data of talent in target fields (skill stacks, project experience, platform activity, etc.) is scraped. Inputted into the job JD requirements provided by clients, large language models are called to perform multi-dimensional matching and scoring on candidates. Standardized candidate panoramic reports including skill matching degree, project overlap degree, and job-hopping willingness prediction are outputted and regularly delivered to cooperative headhunting companies every week. Headhunters only need human review to directly reach candidates, significantly reducing sourcing time costs.

🛠 Setup Requirements

No professional algorithm background is required; mastering basic Python crawler skills is sufficient. Ready-made open-source crawler tools or public APIs of recruitment platforms can also be directly used to lower the threshold. Combined with domestic large model APIs such as DeepSeek and Qwen for information cleaning and matching analysis, and using n8n or Coze to build automated workflows, a closed-loop from data scraping to report generation is achieved. The overall setup cycle does not exceed 7 days, with no need to train models by oneself.

🧰 Toolchain

🔧 GitHub API
🔧 DeepSeek API
🔧 n8n
🔧 Coze
🔧 Feishu Documents

💰 Revenue

① Monthly subscription for candidate reports from small and medium-sized headhunting companies (primary income): Headhunting companies pay a service fee monthly, 5,000 RMB/month × stable 3 companies = 15,000 RMB/month, accounting for about 75% of monthly revenue (estimation: 3 companies × 5,000 RMB = 15,000 RMB ÷ monthly revenue of 20,000 RMB calculated based on 5 companies in this card); ② Expansion and incremental subscription: The 4th to 5th clients are priced at the same 5,000 RMB/month, 5 companies totaling a monthly income exceeding 20,000 RMB (the wording given by this card itself), with an increment of about 10,000 RMB/month; the specific proportion of this part is not separately given; ③ Customization of industry and seniority segmented reports: One-time charges per project. Customized quotes and the number of projects have no public figures, and the proportion of this channel has no public figures; ④ Subsequent opportunities - Productizing candidate matching reports into self-service SaaS: Open-source solutions already support multi-platform job collection and delivery, with a default daily delivery limit of 100 (case narrative source, unindependently verified). The income proportion of this path has no data yet.

💸 Cost

The main expense is the inference cost of job summaries after scraping and parsing, about 300 RMB per month; server and domain name costs are about 100 RMB per month; if using the n8n open-source version, no additional subscription fee needs to be paid, and the total monthly cost can be controlled within 500 RMB, with a profit margin exceeding 95%.

⏱ Time Investment

About 3 hours are invested every day, mainly used for reviewing the accuracy of generated reports, adjusting matching logic parameters, and docking client requirements. An additional 1 hour is invested weekly for client communication and requirement iteration.

🚀 Getting Started

Step 1: First take orders from freelance platforms like Zhubajie, Dianya Community, and Tianxin Workshop, providing 1-2 small headhunting companies with a 3-day free trial report in exchange for real delivery cases; Step 2: After accumulating more than 3 successful cases, raise the public quote to 5,000 RMB/month/company, and expand clients through headhunting industry communities and offline salon referrals; Step 3: Gradually accumulate matching templates for different technical fields to improve service efficiency.

🔑 Keys to Success

✅ The coverage of candidate information must be broad, requiring the integration of GitHub technical talent, LinkedIn professionals, and domestic recruitment platform data to cover multi-field talent pools
✅ Reports need to mark key data sources and human review traces, and provide verifiable supporting information in matching dimensions to build client trust
✅ Delivery formats must be uniformly standardized, supporting import into headhunting companies' existing CRM systems to lower the threshold of use for clients
✅ Value-added services such as talent salary prediction and background checks can be superimposed to increase customer unit price and customer stickiness

⚠️ 风险

⚠️ Recruitment platforms have strict anti-scraping restrictions on automated scraping, posing risks of account bans and IP blacklisting; scraping frequency must be controlled and proxy pools must be used
⚠️ There are privacy compliance risks in the use of public talent information; it is necessary to strictly scrape only data actively made public by users to avoid touching the red line of the Personal Information Protection Law
⚠️ Matching results generated by large models may contain errors. If report accuracy does not meet standards, it will lead to client churn; human spot-check and logic optimization mechanisms must be established

📌 Real Cases

📌 The GitHub open-source project Auto-JobHunter (JerryOctopus/Auto-JobHunter) implements the logic of scraping technical job information from GitHub and automatically matching resumes. Although designed for job seekers, its data scraping and matching technology path can be directly reused in headhunting sourcing scenarios, and technical feasibility has been verified
📌 A 3Ks report in 2025 reported that leading headhunting platform WoWa released the industry's AI-native Agent "Domi," which can achieve automatic sourcing and preliminary screening of talent. After launch, it has served over a thousand headhunting companies, proving that the demand for AI matching tools in the headhunting industry is real and the willingness to pay is strong
📌 The GitHub open-source project JobsIn (Chinaduanyun/JobsIn) supports multi-platform intelligent job collection and resume matching, having currently gained over 1,000 stars, indicating that recruitment-related automation tools have high practical value in the developer community with a low technical implementation threshold

Sources