In 2026, the global AI field ushered in a new round of climax of technology release and application. At the same time, the AI narrative logic for ordinary users is also quietly changing. People are no longer satisfied with the toy-like application of "chatting and writing poems", instead, it is a competition about "landing"-vertical industries such as finance, medical care, law and education are looking for the convergence point of "AI+". In this wave, a long-neglected trillion-dollar market, home service, has finally ushered in its own AI moment.

At the beginning of March 2026, Wanshifu, a home service platform, launched a vertical AI agent for the home service industry-pea AI. This product is based on more than 200 million real orders accumulated by the platform and the service experience of more than 4 million certified masters, aiming at trying to use artificial intelligence technology to alleviate the long-standing information asymmetry problem in the field of home service. China Home Expo believes that the emergence of this vertical AI agent provides a new idea for the transparent upgrade of the home service industry.

The home service industry is characterized by high demand and high information barriers. The demand for house repair, installation and maintenance is widespread, but due to the high technical threshold, consumers are often at an information disadvantage in service transactions, and the price transparency, service standardization and after-sales guarantee level need to be improved. Industry observers believe that this structural feature makes home service one of the potential scenarios for the application of AI technology.

Tian Xiaozheng, the founder of Wanshifu, has publicly stated that the platform has repeatedly seen users fall into anxiety and entanglement when adding furniture or repairing home appliances: Is the found master professional and reliable? Is the service price fair and transparent? Will you sit on the ground and start the price? In his view, the core problems that the platform needs to solve are the uneven skills of masters, the difficulty of on-site supervision and the difficulty of standardizing non-standard services. For users, it doesn't matter what path AI technology takes. The key lies in whether it can really solve the problem-no matter how cool the technology is, it will eventually return to the practical value of "reducing costs and increasing efficiency".

The product function of pea AI is designed around three directions: "avoiding pits, saving money and saving trouble". The agent can provide industry price reference and early warning of common service routines to help users identify unreasonable charges; For some simple problems, a graphic tutorial is attached to encourage users to handle them themselves; At the same time, based on historical order data and user evaluation, the appropriate service master is recommended intelligently. Pea AI is built into Wanshifu's WeChat applet. After describing specific problems, users can get graphic diagnosis guide, industry reference price and master information.

Different from common AI dialogue models such as DeepSeek and ChatGPT, pea AI is special in that it is not a "theoretical school" trained by crawling general knowledge from the Internet, but is based on more than 200 million real orders accumulated by Wanshifu in 12 years and the service experience of more than 4 million certified masters. It is more like an "old master" who has repaired tens of thousands of home appliances. This is the core barrier of pea AI: it is not a generalized Agent framework, but relies on professional deep cultivation, deep industry knowledge precipitation and safe and reliable professional data connectivity.

At present, the pea AI 1.0 version focuses on the scene of home appliance maintenance, aiming at breaking the information asymmetry in this field and focusing on overcoming the core pain points such as door-to-door price increase, arbitrary charges and repeated maintenance. However, home service is a huge and complicated market, covering installation, maintenance, cleaning, maintenance, handling and many other sub-fields, and there are similar information asymmetry problems in each field. The closed-loop industrial logic of "data-diagnosis-quotation-matching" formed by Pea AI is expected to continue to promote industry transparency and intelligent upgrading in other segments in the future.

From a more macro perspective, the emergence of pea AI may be a watershed for the home service industry from "informationization" to "intelligence". During the industry 1.0 period, the platform made scattered master resources online, which solved the problem of "difficult to find someone"; During the period of 2.0, the platform improved the evaluation system, secured transactions and after-sales guarantee, and solved the problem of "difficult trust"; Now entering the 3.0 period, AI is involved in the whole process of service, from diagnosis to matching to pit avoidance, and strives to solve the problem of "difficult decision-making". China Home Expo pointed out that this mode of "ask AI first, then find service" is expected to enhance the overall experience of home consumption.

Pea AI's service model is to provide a step-by-step solution: through intelligent analysis of user problems, systematically give a step-by-step troubleshooting guide. When users inquire about home problems, it will start with a variety of common reasons to help users quickly locate the problems and provide specific solutions one by one. When the user decides to seek the help of the master, it will also provide pit avoidance guide and industry charging reference to break the information asymmetry, so that consumers can accurately identify routines and predict risks in advance.

The traditional platform service process is "user orders-master service-user evaluation", which is an after-the-fact feedback mechanism-the problem has occurred, the service has been completed, and users can only "vote with their feet" or spit afterwards, and the evaluation has obvious lag and subjectivity. The logic of pea AI is completely different: it moves the intervention node from "after the event" to "before the decision-making link". Before looking for a master, users should first diagnose the problem through AI (whether it is a big problem or not), then predict the scheme (probably how to repair it), combine the price expectation (what is the reasonable range), and finally complete the master matching (who is good at repairing this). This decision-making path of "ask AI first, then find the master" is essentially to rebuild the initiative of consumers in service transactions. The user went to the master with information and expectation, and the conversation between the two sides changed from "you have the final say" to "we will discuss it".

The launch of pea AI is an important step for the home service industry to move from general dialogue to vertical deep cultivation in the application of AI technology. Relying on more than 200 million real orders accumulated by Wanshifu in 12 years and the service experience of more than 4 million certified masters, the agent focuses on the scene of home appliance maintenance, builds a step-by-step solution around "avoiding pits, saving money and saving trouble", and moves the information intervention node from the traditional post-event evaluation to the user decision-making link. By providing services such as price reference, fault self-diagnosis and master intelligent matching, Pea AI tries to break the information asymmetry and trust barrier that has long plagued the industry. From the perspective of industry evolution, it has promoted the third-stage leap of home service platform from "online" to "intelligent", and provided an operable sample to solve the problem of "difficult decision-making" after "difficult to find someone" and "difficult to trust". In the future, its closed-loop logic of "data-diagnosis-quotation-matching" is expected to extend to more subdivided fields such as installation, cleaning and maintenance, and help the information circulation mode and consumer initiative in the home service industry to achieve systematic reconstruction.