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AI Supplier Scorecard Subscription Query - Monthly Revenue 15k RMB

Workflow: Daily scheduled crawling of public data sources such as public bidding platforms, industrial and commercial information,

AGENT

Key Fields

FIELD STAMPS

IndustrySaaS / Enterprise Software

RegionChina

ScaleSME

ChannelOnline

🔧 Workflow

Daily scheduled crawling of public data sources such as public bidding platforms, industrial and commercial information, and judgment document networks. Large language models are used to extract structured fields including supplier historical delivery records, quality penalties, and litigation risks, generating multi-dimensional scorecards. Procurement enterprises can input the supplier name to query the comprehensive score, risk warnings, and delivery history, outputting a downloadable scorecard report. Manual reviewers conduct weekly spot checks on 10% of the scoring results, calibrate model weights, and flag anomalous data, while updating the scoring dimensions every quarter to adapt to changing industry procurement standards.

🛠 Setup Requirements

Technical capability requirements are moderate, requiring proficiency in Python crawlers, large language model API integration, and simple frontend page building. Core tools include Coze or Dify to build scoring agents, PostgreSQL to store the supplier database, and n8n to orchestrate scheduled collection workflows. The setup period takes about 2 to 3 weeks, requiring an initial manual annotation of 100 to 200 vertical industry supplier samples as a scoring baseline, which is subsequently optimized automatically for scoring accuracy as data accumulates.

🧰 Toolchain

🔧 Coze
🔧 n8n
🔧 PostgreSQL
🔧 DeepSeek API
🔧 Scrapy

💰 Revenue

① Small and medium-sized procurement enterprise monthly subscriptions (primary revenue): Subscription-based monthly pricing, enterprise monthly subscription of 500 to 2,000 RMB × approximately 30 serviced accounts × retention rate of about 65% (approx. 20 renewed accounts) = monthly revenue of about 15,000 RMB. The exact proportion of total revenue is unspecified (based on case study data, unindependently verified; timestamped 2026); ② Pay-per-query usage billing: Metered billing per query, 10 to 30 RMB per query × query volume unverified, and the exact share is also not provided; ③ Customized scorecard reports for bidding scenarios: Project service fees, with neither individual pricing nor order volume verified, and the market share is similarly blank; ④ Opportunity items (not yet scaled) - Cross-border procurement compliance review subscriptions: Priced by seat or annual authorization licensing, with neither seat prices nor authorization prices publicly disclosed, and revenue potential is currently unknown.

💸 Cost

Main costs include DeepSeek API call fees of about 2,000 RMB/month, cloud server costs of about 500 RMB/month, and basic subscriptions for public data sources of about 800 RMB/month, totaling approximately 3,300 RMB/month, with no additional labor costs.

⏱ Time Investment

Approx. 3 hours invested daily, mainly spent on data collection monitoring, scorecard result spot checks, and responding to customer inquiries, with minor adjustments on weekends.

🚀 Getting Started

The first step is to choose a vertical niche industry (such as construction materials or IT outsourcing), manually compile public delivery records and risk events for the top 200 suppliers in that industry, and establish an initial scoring sample database. Next, publish free trial scorecard reports in industry procurement communities, collect feedback from buyers to optimize scoring dimension weights, and gradually transition to a paid subscription model.

🔑 Keys to Success

✅ Scoring dimension weights must align with the actual decision-making logic of buyers, as delivery evaluation standards vary greatly across industries.
✅ The breadth of data source coverage and update frequency determine the timeliness of the scorecard; litigation and penalty data latency should not exceed 7 days.
✅ The manual spot-check mechanism is the cornerstone of trust, and the prerequisite for buyers to pay is that scores are verifiable and reliable.
✅ Deep cultivation in vertical industries is more effective for building reputation than cross-industry coverage, and single-industry supplier databases feature higher barriers.

⚠️ 风险

⚠️ Public data sources may be interrupted due to platform anti-scraping measures or policy changes, requiring multi-source backups.
⚠️ Misjudgments in supplier scoring could lead to procurement losses for buyers, posing complaint or legal risks.
⚠️ Enterprise willingness to pay is affected by macroeconomic conditions, and procurement digitization budgets may be cut during industry downturns.

📌 Real Cases

📌 The DeepCheck intelligent bid evaluation vertical model has been implemented in bidding scenarios, compressing qualification and performance reviews of hundreds of pages of tender documents from half a day manually to seconds via AI.
📌 The Guozhen AI station has launched a supplier scoring calculator, allowing buyers to input parameters online to generate real-time scores, validating the market demand for automated scoring tools.
📌 CloudVista Consulting's AI supplier risk assessment tool has been applied in cross-border procurement scenarios, helping buyers complete independent compliance reviews before signing contracts and reducing supplier onboarding risks.

Sources