AI Intelligent Quotation System for Factory Energy-Saving Retrofitting

📌 Background

With the accelerated integration of artificial intelligence and energy strategies in 2026, energy performance contracting (EPC) has become the mainstream implementation method for factory energy-saving retrofitting. However, energy service companies previously faced time-consuming preliminary calculations and lacked evidence-based proposals. The AI quotation system compresses this workflow significantly: vendor product pages indicate that after importing electricity bills for the past 12 months, the quotation time per project is compressed from 4.5 hours to 12 minutes (merchant's self-reported metric, not independently verified). Industry-side investments for most EPC projects fall within the range of 500,000 to 50 million RMB (third-party research figures, unverified by our side).

👤 Target Customers

Target customers are energy service companies and factory energy management departments. The direct payers are energy service companies, paying per account or per project.

💰 Revenue Streams

1) Annual software subscription fees; 2) Platform service fees charged as a percentage of the EPC project contract amount; 3) One-time fees for bundled energy-saving retrofit proposal design.

🧮 Cost Structure

Costs for AI energy efficiency model R&D, maintenance of industry energy consumption benchmark databases, sales and implementation teams, cloud servers, and energy consumption data interface integration.

🛡️ Moat

Accumulation of sub-industry energy consumption benchmark data and quotation models, mastery of process-level knowledge in high-energy-consuming workshops such as textiles and dyeing, and deep binding with the project workflows of energy service companies resulting in high switching costs.

🔑 Keys to Success

• Establish verifiable industry energy consumption benchmarks and databases for electricity and steam prices
• Integrate with the project management systems of energy service companies, extending from quotation to execution and revenue-sharing settlement
• Continuously iterate the AI calculation model using closed-loop data from real EPC projects

⚠️ Risks

• Incomplete factory energy consumption data collection leading to quotation deviations and disputes
• Long EPC project cycles, making customer renewal willingness susceptible to payment collection rhythms
• Proliferation of low-threshold quotation tools triggering price wars

🏢 Cases

• Jianjian AI-ERP Factory Energy-Saving Retrofit AI Intelligent Quotation System

📊 SWOT Analysis