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Selling white-label AI motion correction APIs to health software generates 25,000 RMB/month based on call volume

Workflow: Automatically handle user motion video streams pushed by B-end health software and fitness mini-programs every day. The

AGENT

Key Fields

FIELD STAMPS

IndustryContent / Creator Economy

RegionChina

ScaleSME

ChannelOnline

🔧 Workflow

Automatically handle user motion video streams pushed by B-end health software and fitness mini-programs every day. The AI vision agent first analyzes human skeleton nodes and compares them with the built-in standard exercise action library to generate preliminary correction plans and progress tracking reports. Subsequently, contracted certified fitness trainers act as referees to manually review and revise the plans, which are finally automatically transmitted back to the B-end frontend to be presented to users. The entire basic process requires no manual intervention and can serve multiple clients simultaneously.

🛠 Setup Requirements

Setting up requires mastering private deployment capabilities of open-source pose estimation frameworks, being familiar with large model vision API calling and cloud function high-concurrency scheduling logic, and building a simple trainer review backend. Encapsulating the core API and building the basic action library takes about 2 weeks. No special hardware procurement is required in the early stage, and ordinary cloud servers can support the initial call volume. Once verified, it can be packaged as a black-box product and delivered to clients.

🧰 Toolchain

🔧 MediaPipe open-source pose estimation framework
🔧 GLM vision large model
🔧 Alibaba Cloud Function Compute
🔧 Object Storage Service (OSS)

💰 Revenue

① API call billing (main revenue): B-end health and fitness software providers pay based on the number of calls, with an average price of 0.005 RMB/call. 50,000 calls per day = about 250 RMB/day, about 7,500 RMB/month (estimated value). The exact proportion in total revenue is not specified (case study data, independently unverified, caliber year 2026); ② B-end basic service fee: The platform charges a monthly basic service fee. Combined with ①, the single-person monthly income stabilizes above 25,000 RMB. The exact amount of the basic service fee is not disclosed (also case study data, unverifiable externally), and its revenue share is likewise untraceable; ③ Annual framework subscriptions for head clients: Paid on an annual framework basis, head clients' annual contracts can exceed 300,000 RMB (about 25,000 RMB/month). The exact number of such head clients has not been verified (case material, lack of independent review), and their share is also impossible to determine; ④ Opportunity item - Small studio training program subscription: Sources claim this version is suitable for small studios with fewer than 30 members (media estimates, lacking independent verification). Pricing details and revenue share are both unclear.

💸 Cost

Large vision model calling costs about 0.002 RMB/call, peak cloud function scheduling and storage expenses total about 5,000 RMB per month, trainer review labor costs are about 3,000 RMB per month, and total costs are about 8,000 RMB/month.

⏱ Time Investment

2 hours per day, mainly used for system monitoring, trainer review of difficult cases, and communication with new client requirements.

🚀 Getting Started

The first step for beginners is to build a basic pose recognition demo based on the MediaPipe open-source framework, running through the complete process from video stream input to action correction report output. The second step is to organize standard action libraries for 3-5 niche scenarios (such as shoulder and neck relaxation, home fat burning, postpartum recovery), and directly approach small and medium-sized health app and fitness mini-program developers with the demo to discuss commercial cooperation.

🔑 Keys to Success

✅ A closed-loop of structuring and fault-tolerant fallback rules for the sports standard library, covering action correction logic for different groups of people and different scenarios
✅ High-concurrency cloud function scheduling mechanism to prevent heavy computing from dragging down servers and driving up costs in high-concurrency scenarios
✅ B-end business channel expansion pitching scripts that precisely hit the pain points of health products in improving user retention
✅ Trainer review SOP standardization to improve manual review efficiency and reduce labor costs

⚠️ 风险

⚠️ User motion videos involve private data, and the circulation process must comply with the Personal Information Protection Law, triggering regulatory risks
⚠️ High visual recognition error rates in low-light and occluded environments, which may lead to compensation risks for user injuries caused by incorrect movements
⚠️ The risk of losing orders after B-end clients replace third-party services with their own self-developed motion features

📌 Real Cases

📌 Lovenfit launched the Jackie home training course containing 6 training systems and 60 action demonstrations, verifying user willingness to pay for AI motion guidance
📌 Ping An Good Doctor and BodyPark jointly created an AI plus human personal trainer service, verifying the path of B-end health platforms procuring AI motion capabilities for monetization
📌 The MOGE platform AI exercise generator reviewed personalized plans through certified trainers, verifying the feasibility of the closed-loop logic of AI generation + human referee

Sources