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Zhidemai Consumption Data Platform "Data × Agent" Two-Way Authorization

1) Pay-per-use billing for data authorization and MCP calls; 2) Brand and merchant data service fees; 3) Continued paral

MODEL

Key Fields

FIELD STAMPS

IndustryE-commerce / Retail

RegionChina

ScaleMid-size

ChannelHybrid

📌 Background

In 2026, large language model agents are replacing general search at scale, and consumption decision-making scenarios require a large amount of real-time, structured product and word-of-mouth data that can be called programmatically. Zhidemai forms its data asset from the product databases, promotional information, and consumer guides accumulated by "Smzdm" (What to Buy), with quarterly content output exceeding 1 billion times. Through the MCP protocol, it outputs data to external agents, becoming a successful practical example of a consumer data intermediary.

👤 Target Customers

Brand owners, e-commerce platforms, LLM vendors, and consumer research institutions. The payers are AI applications requiring consumer profiles and product knowledge supply, along with the commercial institutions behind them.

💰 Revenue Streams

1) Pay-per-use billing for data authorization and MCP calls; 2) Brand and merchant data service fees; 3) Continued parallel operation of original shopping guide commissions and advertising revenue.

🧮 Cost Structure

Content production and UGC incentive costs, data cleaning and compliance review costs, server and API R&D costs, and brand business development costs.

🛡️ Moat

High-quality structured data accumulation in vertical consumption decision scenarios, long-term community content deposition, merchant cooperation networks, and the real-time update of data.

🔑 Keys to Success

Accelerate the integration of MCP endpoints for more agent platforms
Establish a compliant and flexible data pricing and authorization system
Maintain the update frequency and structural degree of consumption content

⚠️ Risks

Stricter data compliance reviews, with tightening boundaries for personal information and consumer behavior data
LLM vendors shifting to self-collected or free data sources
Concentration risk among MCP protocol integration partners

🏢 Cases

Zhidemai (Listed on Shenzhen Stock Exchange ChiNext)
Daily Interactive (Data Intelligence Service Provider)

📊 SWOT Analysis

Strengths

Possesses exclusive consumption decision scenario data, with quarterly content output exceeding 1 billion times
MCP protocol integration transforms data from human-readable to machine-readable, opening up new channels for data monetization

Weaknesses

Traditional shopping guide commissions and advertising revenue remain the base, and the revenue share of data services still needs to increase
Strong reliance on the traffic rules of upstream e-commerce platforms

Opportunities

LLM vendors compete for high-quality consumer data, and agent calls become a new growth driver
Improvement of data rights confirmation and transaction policies helps form standardized data products

Threats

Platforms like Alibaba and JD.com building their own closed-loop data ecosystems may squeeze the space for third-party data intermediaries
General-purpose LLM vendors directly scraping public content, weakening data scarcity

Sources