AI breaks through the pain points of construction cost calculation, and Zhisuan Costing is seeking seed round financing.
The annual output value of China's construction industry exceeds 30 trillion yuan, of which the cost of installation engineering accounts for about 25% to 30%, corresponding to a market size of 7.5 trillion to 9 trillion yuan. However, the digital penetration rate in this field is less than 10%, and the penetration rate of AI tools is less than 5%. The compilation of installation engineering cost has long relied on manual quantity calculation and empirical judgment. A bill of quantities of medium scale often takes 3 to 5 days to complete, and there is a lot of repetitive work in links such as quota matching, equipment inquiry, and format conversion. Against this background, the Smart Cost team has developed an AI-driven automation platform for installation engineering cost, trying to reconstruct the core workflow of cost compilation through AI technology, covering five majors: electrical, HVAC, water supply and drainage, fire protection, and intelligence. Starting from the most painful link of bill compilation, it will gradually realize AI automatic measurement and pricing for each major and each part.
Structural Efficiency Bottlenecks Faced by Installation Engineering Cost Compilation
Installation engineering cost compilation is a field that highly relies on professional experience. Cost engineers need to complete multiple links in their daily work, such as drawing identification, circuit extraction, quota matching, equipment inquiry, quantity calculation and result output, each of which requires a lot of manual operations. According to the survey of the Smart Cost team, a cost engineer with more than 3 years of experience usually takes 10 to 15 working days to compile a medium-scale installation engineering bill, of which more than 60% of the time is consumed in repetitive data transfer and format conversion.
The core pain points faced by the industry are concentrated at several levels. First, the efficiency of manual quantity calculation is low, cost personnel need to identify circuits and count the number of equipment on each drawing, which is heavy workload and error-prone. Second, quota matching is highly dependent on experience, thousands of quota sub-items need to be selected by senior engineers based on memory and experience, the onboarding cycle for new employees usually takes 5 to 8 weeks, and there is an obvious gap in knowledge inheritance. Third, the equipment inquiry information is severely fragmented, the prices of ABB, Schneider and other brand equipment are scattered in WeChat groups, call records and various websites, the quotation difference of the same equipment in different channels can reach 3 to 10 times, which is difficult to compare quickly. Fourth, the output format of results is cumbersome, cost personnel need to switch repeatedly between the three formats of Glodon, Jianjing Technology and Excel, and the repetitive work caused by repeated input of the same data exceeds 40%.
From the policy perspective, the Ministry of Housing and Urban-Rural Development has clearly required that the application of BIM + AI technology in the field of engineering cost be fully promoted by the end of 2025, and the digital transformation of the industry has entered an accelerated period. More than 2 million registered cost engineers across the country and a larger number of practitioners constitute a huge potential user base. However, most of the existing cost software stays at the level of process management and data recording, and cannot directly generate deliverable engineering results. The market urgently needs an AI productivity tool that can cover the whole link from drawing identification to result output.
Reconstruct the Cost Compilation Process with AI Automatic Measurement and Pricing
The core product of Smart Cost is the electromechanical AI intelligent measurement and pricing platform, which is positioned to "change the current situation of the cost industry" and realize AI automatic measurement and pricing for each major. At present, the platform has completed three modules, and more than 20 modules covering all majors and all divisions of installation will be built later: the completed modules include the automatic grouping and pricing module for air outlets and valves, the automatic grouping and pricing module for fan equipment, and the automatic grouping and pricing module for distribution boxes.
The core capability of the pricing module lies in automatically identifying the quantity information of each major, matching the quota code according to the 2025 Tianjin pricing specification, realizing 100% correspondence between the code and the name, and built-in the price library of each installation major, which can obtain the latest quotation in real time and automatically generate the pricing result. It can quickly associate quota sub-items to automatically calculate the quantity, and the equipment column in the exported result is automatically marked as "equipment under sub-item", which directly meets the data format requirements of mainstream cost software such as Glodon.
In terms of technical architecture, the bottom layer of the Smart Cost platform consists of four major engines. The AI intelligent analysis engine is responsible for the identification and understanding of drawings and documents, which can complete the drawing identification work that traditionally takes several hours in minutes. The rule engine is built with all the quota standards of Tianjin 2025 version to ensure the compliance and accuracy of the pricing results. The equipment inquiry database has accumulated equipment price data of more than 10 categories, supporting real-time price comparison and historical price traceability. The multi-format export engine supports one-click export of three mainstream formats: Glodon, Jianjing Technology and Excel, eliminating the workload of repeated entry.
In addition to the core electromechanical platform, the Smart Cost team has also developed an automatic compilation system for estimation and budget as an auxiliary tool. The system supports uploading project specifications in Word format, and the AI automatically parses and extracts parameters, which are displayed in a structured way according to the five majors of electrical, water supply and drainage, HVAC, fire protection and intelligence. At the same time, the bill is divided strictly by building number - each building above the ground has an independent bill, and the basement is compiled separately as an independent zone - to ensure that all equipment (water supply pumps, high and low voltage cabinets, transformers, fans, etc.) are completely extracted without omission. According to the team, the platform can improve the efficiency of cost compilation by about 10 times in actual use, reduce the error rate by about 90%, and the entry threshold is reduced from the traditional 5 to 8 weeks of training to uploading documents for use.
At present, the Smart Cost platform has completed the initial construction, and a professional team is testing the three completed modules of air outlets and valves, fan equipment and distribution boxes and improving the database, and it has been gradually put into use in formal cost projects. Later, while improving the existing modules, we plan to build and make breakthroughs in the pricing direction for the remaining professional modules of the installation. In terms of measurement, we plan to optimize and adjust the drawing identification and automatic model generation direction combined with the existing functions of Glodon, to assist cost engineers to further improve the efficiency of budget compilation. It is believed that in the near future, the cost industry will usher in a major change in the overall pattern. After the official release of the Smart Cost platform, the industry efficiency will be increased by more than 90%, changing the current problems of large manpower demand, cumbersome work and high error rate of companies in the industry. Only senior industry personnel need to be retained to conduct a secondary review of the bill compilation work completed by AI.
Focus on Vertical Tracks, from Tool Verification to Commercialization
Smart Cost chooses the vertical track of installation engineering cost as the entry point, rather than a generalized engineering cost platform. The team believes that the installation engineering cost has high professional barriers, intensive repetitive labor and low digital penetration rate, which is a segmented field where AI tools can most easily produce significant efficiency improvements. At present, the three majors of electrical, HVAC and water supply and drainage have been verified, and the follow-up plan is to expand to fire protection and intelligent majors to achieve full coverage of the five majors.
In terms of business model, Smart Cost plans to adopt a strategy of starting from free tools and gradually transitioning to paid services. In the early stage, it provides free basic function experience through the online platform to attract target users such as cost consulting companies, construction units and individual cost engineers; in the middle stage, it launches paid services such as enterprise-level customized development, private deployment and continuous technical support; in the long run, it plans to build a multi-tenant SaaS architecture and open API interfaces to support third-party integration, forming a platform-based business ecosystem.
Founder Li Guanya is currently a cost consulting engineer at Tianjin Architectural Design Institute Co., Ltd., who has been engaged in installation engineering cost, bidding and tendering for a long time, and has rich front-line experience in construction budget and estimate, bidding and tendering. These industry cognitions are directly transformed into the functional design of the product and the logical basis of the rule engine, which enables Smart Cost to have differentiated advantages in the accuracy of quota matching and the practicability of equipment inquiry.
In terms of project progress, the platform has completed core function development and launched operation, the drawing and document identification capability of the AI analysis engine has become mature, the rule engine has achieved 100% coverage of Tianjin 2025 version quota, and the equipment inquiry database has been continuously expanded to more than 10 categories. In the next stage, the team plans to promote the function expansion of water supply and drainage, fire protection, intelligence and other majors, the development of multi-tenant SaaS architecture and mobile terminal adaptation, and simultaneously seek seed round financing. The funds will be mainly used for product iteration R&D, core team building and market promotion.
The Smart Cost team said that the pain points in the field of installation engineering cost are real problems on the industrial side, and the application of AI technology in this field is still in a very early stage, first movers have the opportunity to build long-term barriers through industry data accumulation and rule precipitation. The team's goal is to build Smart Cost into the AI infrastructure in the field of installation engineering cost, so that every cost engineer can be liberated from repetitive work, focus on more valuable work, and finally promote the efficiency reform of the entire cost industry.