← Sticker Wall AGENT · DETAIL

AI Jewelry Appraisal Initial Screening Agent: Certificate + Real Photo Pre-audit, Monthly Revenue of 80,000 RMB

Workflow: Buyers send the agent the real photos of the jewelry to be purchased, certificate photos, and the seller's specification

AGENT

Key Fields

FIELD STAMPS

IndustryContent / Creator Economy

RegionChina（华中）

ScaleSME

ChannelOnline

🔧 Workflow

Buyers send the agent the real photos of the jewelry to be purchased, certificate photos, and the seller's specification description. The system automatically completes the following steps: Step 1 enhances the real photo and extracts the main gemstone region; Step 2 performs OCR recognition on the certificate to extract key fields such as the appraisal institution, certificate number, gemstone type, weight, color, and clarity; Step 3 cross-compares the certificate fields with the visual features of the real photo (e.g., if the certificate indicates ice-type jadeite but the real photo has excessively high light transmission or abnormal color distribution, it is marked as high risk); Step 4 outputs a risk-graded report, including preliminary judgment of certificate authenticity, appearance anomalies, and recommendations on whether manual re-inspection is needed. Buyers decide whether to proceed with a formal appraisal or give up directly based on the report. Running through the four steps of image preprocessing, certificate OCR, feature comparison, and conclusion generation daily, the processing time per item is about 3-5 minutes, and 30-50 orders can be stably processed per day. The system saves each judgment result and regularly feeds back manual re-inspection or actual transaction feedback to form continuously optimized feature rules.

🛠 Setup Requirements

Requires the ability to call vision large model APIs for image recognition, while integrating certificate OCR tools and a jewelry appraisal rule library. Individual developers can use low-code Agent platforms combined with third-party APIs without needing to build models from scratch. Initial development time is about 2 weeks, mainly for debugging certificate field extraction templates and gemstone appearance feature comparison logic. It is recommended to prepare 500-1000 tagged real photos and certificate samples for rule validation, ensuring stable cross-comparison logic between certificate fields and appearance features before providing external services. In terms of cost, it is mainly API call fees, calculated at 0.5-1 RMB per item, with initial monthly costs controllable within 3,000 RMB. WeChat mini-programs or WeChat Work bots can serve as entry points for buyers to submit images and receive reports, lowering the usage threshold.

🧰 Toolchain

🔧 Vision Large Model API
🔧 Certificate OCR Tool
🔧 Low-code Agent Platform
🔧 WeChat Mini-program or WeChat Work Bot
🔧 Jewelry Appraisal Rule Library

💰 Revenue

① Jewelry buyer per-item pre-screening (main revenue): buyers pay per piece, 10-20 RMB per piece × 30-50 orders/day = monthly income of 9,000-30,000 RMB (derived from figures in the card; the case party's caliber has not been independently verified), and the proportion of this channel in total revenue is not public; ② High-value category deep pre-screening: buyers pay a markup per piece for high-value goods, 50-100 RMB/order (from the card), transaction counts cannot be verified, and the share is similarly unknown; ③ Buyer monthly subscription: high-frequency buyers subscribe monthly for unlimited initial screenings, with neither pricing nor subscriber counts publicly disclosed, and its proportion of the total pie is unpublicized; ④ Opportunity item: per-usage API authorization for certificate plus real photo pre-audit provided to small merchants and live-streaming rooms, benchmarked against a Nantong practitioner who accumulated over 2,300 orders and actual received revenue of 1.17 million RMB in eight months (media estimated figure, not independently verified), with the proportion of this channel undisclosed.

💸 Cost

API call fees are about 3,000 RMB/month, where visual recognition is billed per use and OCR is billed per page. Low-code platform subscription is about 300 RMB/month. If commercial vision model prices are higher, the cost per item may increase to 1-2 RMB, with maximum monthly costs around 5,000 RMB.

⏱ Time Investment

3-4 hours per day, mainly used for reviewing high-risk reports, replying to buyer inquiries, and feeding back manual re-inspection results to optimize the rule library. The proportion of automated system processing is high, and individuals can operate it as a side hustle.

🚀 Getting Started

Step 1: Contact 1-2 local jewelry buyer groups or small merchants, organize common certificate fields and gemstone appearance features into a rule table, and build a minimum viable product (MVP) using off-the-shelf vision models. The prototype only needs to support certificate OCR plus real photo color/texture comparison, and output simple risk warnings. After a trial run, iterate the rules based on buyer feedback, starting with a single category like jade and jadeite, as buyer demand in this category is concentrated and the case has been validated. Expand to colored gemstones and pearls after gaining proficiency.

🔑 Keys to Success

✅ Cross-validation logic between certificates and real photos; cannot rely on a single information source
✅ Buyer repurchases and referrals, building trust through accuracy
✅ Transparent risk grading; initial screening only provides risk warnings and does not replace formal appraisal reports
✅ Rapid iteration of the gemstone feature library, turning common fake features into rules
✅ Cold start from local buyer communities to lower customer acquisition costs

⚠️ 风险

⚠️ Misjudgment by a single vision model; high-value goods still require manual review, and liability boundaries must be clearly stated
⚠️ Buyers using initial screening conclusions as formal appraisal results, leading to disputes
⚠️ Upgraded certificate forgery methods; OCR and image comparison failing to keep up with new tricks
⚠️ Decline in reputation after misjudgments in high-value categories, requiring compensation or disclaimer mechanisms to be set up

📌 Real Cases

📌 A Henan man used AI to appraise jade, earning over 1 million RMB in 8 months with a 95% accuracy rate, serving downstream buyers and small merchants
📌 Global Jade Network publicly explained the principles of AI jade appraisal, used for deep learning image recognition to assist initial screening
📌 Yujiantong Baihai Shuzhi provides a jewelry appraisal assistant Agent for pre-audit before buyer purchasing
📌 Tuding AI released a jewelry commodity image acceptance form FAQ, explaining the implementation process of the certificate and real photo cross-validation chain

Sources