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SME Resume Auto-Scoring and Interview Q&A Generator—Solo Monthly Income of 20K RMB

Workflow: Every day, HR batch-uploads received resumes to a designated cloud drive folder, or automatically synchronizes them to t

AGENT

Key Fields

FIELD STAMPS

IndustryEducation / Knowledge

RegionChina（国内）

ScaleSME

ChannelOnline

🔧 Workflow

Every day, HR batch-uploads received resumes to a designated cloud drive folder, or automatically synchronizes them to the agent workspace via WeChat Work/email. The AI automatically extracts dimensions such as education background, years of experience, core skills, and project matching degree based on preset job profiles to score and rank them. Simultaneously, it generates personalized interview questions and reference answer drafts for candidates in the top 30%, finally outputting a structured scoring report + interview Q&A list for HR's manual final review before direct use in interviews.

🛠 Setup Requirements

Can be built using low-code tools like Dify/n8n without programming from scratch. Core technologies include the resume parsing module, LLM API calls, and job profile prompt tuning. Requires preparing 10-20 historical recruitment resumes for the same position to fine-tune scoring rules. The setup cycle is about 2-3 weeks. No need to research and develop models independently; mastering basic API integration and prompt engineering skills allows individual developers to complete it independently.

🧰 Toolchain

🔧 Dify Low-Code Platform
🔧 n8n Workflow Tool
🔧 Claude 3.5 Sonnet API / Qwen API
🔧 Open-Source Resume Parsing SDK
🔧 Feishu Multidimensional Table

💰 Revenue

① SME HR Position Preliminary Screening Package Subscription (Main Revenue): Enterprise HR pays a monthly package fee for preliminary screening. Screening 50-100 resumes per package at 2,500 RMB/month × serving 10 SMEs = monthly income of 25,000 RMB (converted), covering the average monthly income of 20,000 RMB in the case, accounting for about 100% (converted based on case data, merchant self-stated, not independently verified); ② Interview Q&A Customization and Recruitment Process Consulting: Signed enterprises pay additional value-added service fees per incident or per project. The case states this can increase income by over 30%, converting to an increment of about 6,000 RMB/month based on the 20,000 RMB base (converted), the proportion of this part is not given; ③ AI Interview and Written Test Assistance for Job Seekers: Job seekers pay by point quotas. Original pricing is 100 points worth 10 RMB ≈ 10 answers or a 30-minute interview, converting to about 1 RMB/time (converted), which does not conflict with SME main revenue (platform public pricing), the proportion of this sub-item is not given; ④ Opportunity Item - Multi-Industry Job Profile Template Authorization: Charging regional recruitment outsourcing and labor agencies by template or seat authorization. The source case states AI can independently complete 80% of preliminary screening, with no public data on authorization price or proportion.

💸 Cost

The monthly calling cost of LLM APIs is about 1,800-2,200 RMB, calculated based on processing 200 resumes daily. The open-source resume parsing SDK is free to use; if opting for the paid version, it is about 500 RMB monthly. Cloud server and domain name annual fees are about 500 RMB, averaging less than 50 RMB per month. Total monthly costs are controlled within 2,500 RMB.

⏱ Time Investment

Invest 3-4 hours daily, including 1 hour for handling customer feedback and adjusting job profile templates, 2 hours for monitoring AI scoring accuracy and optimizing prompts, and the remaining time for handling new customer onboarding requirements.

🚀 Getting Started

Beginners first lock in 1 high-frequency recruitment general position (such as sales, customer service, e-commerce operations), build a basic scoring template using Dify, and run through the whole process using 10 real resumes from HR contacts at startups around them to produce a verifiable scoring report and interview Q&A sample. Then, penetrate fewer than 3 small enterprise clients via a first-month free trial. After verifying willingness to pay, replicate to other industry positions in batches.

🔑 Keys to Success

✅ Multi-industry job profile templates can be reused and accumulated, reducing new customer onboarding costs
✅ HR's final interview decision acts as a manual judgment closed loop to avoid AI mismatch risks
✅ Scoring basis is transparent and traceable, building customer trust and reducing renewal concerns
✅ Subscription model paid by position and quarter matches SME budgets
✅ Continuous accumulation of vertical industry interview question banks and evaluation standards forms product barriers

⚠️ 风险

⚠️ When enterprises have high requirements for precise recruitment, model scoring deviations may cause high-quality candidate mismatches, triggering customer claims or trust loss
⚠️ B-end customer decision chains are long, with an average cycle of 15-30 days from contact to signing, requiring continuous follow-up and deep penetration
⚠️ If LLM API services increase in prices or content compliance issues occur, it may affect product delivery stability and customer retention rates

📌 Real Cases

📌 Bello First-See AI Interviewer supports automatic resume parsing, job matching scoring, and intelligent interview question generation, claiming to help enterprise HR save 90% of preliminary screening time, having served nearly a thousand small and medium-sized manufacturing and retail enterprises as well as multiple Fortune 500 clients
📌 OfferStar Online AI Written and Interview Assistant covers recruitment scenarios such as administrative aptitude tests, personality assessments, and programming questions, generating real-time interview response suggestions. In its first year online, it cumulatively served over 20,000 job seekers and 500+ SME HRs
📌 Yonyou Dayi AI Interview Software is an early AI tool in China deployed in recruitment scenarios, supporting batch resume scoring, interview Q&A generation, and anti-cheating detection, occupying about 12% share in China's recruitment SaaS market with clients covering a large number of small and medium tech and service enterprises

Sources