Xuanshu AI builds a seven-dimensional intelligent system to solve the pain points of entity management and control and seeks financing

1. Core Pain Points and Market Demands

Domestic physical entity digitization has gone through the stages of single - point collection 1.0 and passive management 2.0. The industry is in urgent need of an AI active governance 3.0 system. Currently, high - end precision manufacturing and high - end hotel consumption enterprises generally face pain points such as insufficient industry knowledge of general large models, high customization and transformation costs, fragmented scheduling of multiple devices/robots, isolated islands of enterprise operation data, dependence on the network of traditional cloud solutions, and risks of privacy leakage in data upload. Traditional digital tools can only achieve single - point control and cannot complete full - link autonomous optimization. By 2026, the market scale of industrial AI integration will reach the trillion - level. The supply of full - domain active intelligent solutions covering the two major tracks of high - end consumption and precision manufacturing is extremely scarce, and there is sufficient incremental market space.

2. Product Technology Solutions and Core Capabilities

Sichuan Xuanshu Artificial Intelligence has independently developed a seven - dimensional integrated AI3.0 full - link autonomous governance closed - loop architecture. The seven core components work in synergy: Xuanshu Core underlying large model base, Xuanshu Agent multi - intelligent agent scheduling system, SaaS full - domain business middle platform, Prompt intelligent execution engine, AIoT Internet of Things perception hardware, multi - robot collaborative scheduling module, and vertical industry - specific knowledge graph. It fully covers the entire business link of perception, decision - making, execution, and iteration. There is no direct comparable product in the track, and the multi - layer technology superposition forms an exclusive barrier with high thresholds and is difficult to replicate.

1. Advantages of Lightweight and Rapid Deployment

It is compatible with the enterprise's original legacy systems such as hotel PMS and factory ERP. There is no need for large - scale hardware replacement. The overall project implementation cycle is shortened by 75%, and the comprehensive transformation cost is reduced by 60%. It supports lightweight batch replication and delivery for single stores and single factories, and can quickly complete project launch.

2. Advantages of Edge Local Data Security

It adopts a private local edge computing architecture. The enterprise's customer, operation, and production data are stored locally, ensuring that the data does not leave the internal network domain and avoiding potential data leakage risks caused by cloud transmission.

3. Advantages of Continuous Operation during Power Outages

In the event of a sudden power outage, the system can continue to operate independently for 8 - 12 hours, ensuring the uninterrupted operation of core business such as enterprise energy consumption control, passenger flow operation, and equipment scheduling.

4. Advantages of Millisecond - Level Real - Time Response

Relying on local edge reasoning, instructions for energy consumption scheduling, passenger flow operation, and equipment control are issued in milliseconds, meeting the real - time control needs of hotel front desks and factory production lines.

5. Advantages of Vertical Industry Business Barriers

It has an exclusive in - depth exploration of vertical knowledge graphs in the hotel and PCB precision manufacturing tracks, making up for the short - board of general AI industry knowledge. It can output autonomous operation optimization strategies suitable for offline physical business forms, which are difficult to replicate for similar products.

6. Advantages of Unified Scheduling Ability for Multiple Terminals

It can quickly build vertical - scenario - specific intelligent agents to uniformly schedule multiple terminal devices and multi - robot collaborative business modules, achieving full - process unmanned autonomous control.

3. Business Model, Target Users, and Market Space

1. Core Target Users

Large and medium - sized precision PCB manufacturing enterprises, chain high - end hotels and other physical consumption stores.

2. Sustainable Profit Model

The revenue is divided into three major sections: annual authorization service fees for the middle platform, scenario - specific customization and development fees, and long - term operation and maintenance service fees. The gross profit margin of the standardized middle platform exceeds 80%, and the gross profit margin of customized services exceeds 50%. Standardized replication can continuously reduce marginal expansion costs, and the overall cash flow is relatively stable.

3. National Market Expansion Plan

The enterprise aims to be based in Sichuan and radiate across the country. In terms of the market, currently, we have a national full - scenario and full - station integrated solution, and there are no similar competitors in the industry. The advantage of large - scale replication is prominent. Taking Sichuan as the base, we will first cover large - scale physical enterprises in the province, and then radiate to the national manufacturing and high - end consumption markets, continuously expanding group - based and chain - based large customers.

4. Team Background and Project Implementation Verification Progress

1. Introduction to the Core Team

Wang Lizhi is the founder of Sichuan Xuanshu Artificial Intelligence Technology Co., Ltd. He has full - domain operation experience in physical industry digitization and leads the entire process of R & D and commercialization of the seven - dimensional intelligent system.

2. Implementation and Verification of a High - End Hotel Benchmark (Complete Data for Two Cycles of a Quasi - Four - Star Business Hotel in Chengdu)

The case data are all taken from the hotel's internal official financial statements. The cost - reduction and revenue - increase ability is verified in two complete operation cycles. The total investment in project hardware and software is 550,000 yuan, and the cost can be recovered in 20 days. The profit model can be replicated in batches, and there are also five - star stores in Chengdu waiting for delivery.
First operation year (June 2024 - May 2025)
The hotel's total annual revenue is 80 million yuan; the benchmark loss rate is 19%, which drops to 12% after the system is implemented, a net decrease of 7 loss percentage points; the annual loss savings is 5.6 million yuan; AI brings in an additional 20,000 in - store customer visits; the additional net profit is 4 million yuan; the total cost - reduction and efficiency - increase amount is 9.6 million yuan.
Second operation year (June 2025 - May 2026)
The hotel's annual revenue increases to 89 million yuan; the benchmark loss rate is 19%, which drops to 9% after optimization, a net decrease of 10 loss percentage points; the annual loss savings is 8.9 million yuan; AI brings in an additional 22,000 in - store customer visits; the additional net profit is 4.6 million yuan; the total cost - reduction and efficiency - increase amount is 13.5 million yuan.
During the off - season, 70% of the passenger flow increase comes from AI targeted customer acquisition. During the peak season, AI only activates repeat purchases from existing customers. The differentiated investment strategies for off - peak and peak seasons have achieved remarkable results.

3. Implementation Progress in the Precision Manufacturing Industry

It has connected with a 4,000 - person PCB factory in Daya Bay. The solution integrates procurement, the entire process flow, and standard operations, comprehensively optimizing multiple losses in materials, manpower, and organization, and achieving comprehensive cost - reduction and efficiency - increase. At the same time, it is in negotiation with a leading listed electrical enterprise in Wenzhou for a full - domain digital upgrade. It is planned to complete the pilot delivery of a PCB intelligent factory by the end of 2026, and the overall deployment efficiency is better than the enterprise's self - developed digital solution.

5. Current Round of Financing Plan

The project is simultaneously applying for a 3 - million - yuan science and technology innovation special loan and plans to introduce 5 - million - yuan institutional financing, offering 10% equity. The fund allocation is as follows: 60% for R & D of the large model and intelligent body core; 20% for national market benchmark expansion; 20% for core talent introduction and long - term team incentives.

6. Long - Term Development Trend of the Project

When talking about the long - term layout, Wang Lizhi, the founder of Sichuan Xuanshu Artificial Intelligence Technology Co., Ltd., said that the enterprise has long adopted a joint R & D model of industry, academia, and research. After the business scale meets the standard, it will cooperate with artificial intelligence colleges and research institutions to build an integrated cooperation system of industry, research, and learning, deeply iterate the Xuanshu Core large model, industry knowledge graph, and AIoT hardware, and provide standardized intelligent body solutions for chain and group enterprises. On the industrial side, it will closely follow the national digital economy and intelligent manufacturing strategies, rely on the local base in Sichuan to continuously radiate across the country, empower physical enterprises to complete intelligent transformation, accelerate the industry's full entry into the AI active governance 3.0 stage, and contribute to the digital upgrade of the domestic physical industry.