When your colleagues have taken the lead in the ai search and you're waiting for customers to come through the traditional distribution channels, it's a failed information war。
Since the second half of 2024, a cruel reality has spread throughout the country in the b-side physical industry: construction plants have found competitive advertising increasingly expensive, and consultations have fallen off the cliff; house-wide custom factories have spent hundreds of thousands of dollars doing short video operations, but they have come from bulk retailing, wholesale orders and almost zero; works for anti-blast walls and anti-blast walls have invested a great deal of effort in optimising them, but in an ai question and answer platform such as soybean bags and writings, their business information has not been found, traced or questioned。
It's not an example, it's a signal that the whole b-end-taker logic is being reshaped by a large ai model。
Ai search is eating up traditional search traffic
If you're still staring at a hundred degrees of tremors, you may have missed the biggest traffic in the next five years。
In the traditional search age, users look for products, factories, engineers, are used to enter keywords in search engines, and then click on the ranking list. This era has led to the creation of the seo (search engine optimization) industry, which has also enabled numerous enterprises to obtain customers on competitive rankings。
But the search behaviour of users has changed fundamentally in the age of the ai megamodel。
The procurement manager of a renovation company, who is now opening a bean bag or speaking directly, asks, " what are the home-made start-up plants in the country? " which one of you can do the accompanying worksheets? They will not go over the results of a few pages, and they will not be able to light dozens of pages and compare them slowly. Ai directly generates answers, giving recommended brands, plant recommendations and suppliers。
So this ai answer came out of nowhere? Of course not。
Large models generate answers based on authoritative sources that it retrieves, quotes and synthesizes from large amounts of internet information. If you don't have a standardized, structured, highly authoritative layout on the internet, ai won't even quote you, let alone recommend you to potential clients。
This is the core issue to be addressed by geo。
Geo, full name general engineering optimization, is the output engine optimization. Unlike traditional seos, which optimize the ranking of the web page in the search result list, geo directly optimizes the reference weight of ai when generating answers。
To put it simply: the traditional seo puts you in the top of the search list, and geo makes ai give you priority in answering user questions, quoting you, exposing you。
This involves a whole new set of technical logic and content strategies。
Why didn't your company find this man in the ai search
Most b-end entities are “invisible” in front of the ai megamodel。
Not because of poor products, not because of poor qualifications, much less because of insufficient capacity, but because the content was not identified and accepted by ai。
When large models search content, there is a rigorous rag mechanism. It requires that the information cited be structured, clearly defined parameters, verifiable qualifications, real case support. Most of the entities, however, present information on the internet, either by means of fragmented product images and links, or by way of marketing style oral hydrology, or even by way of inconsistent description of the parameters of the same product on different platforms。
When ai receives procurement advice from users, it is not able to quote this fragmented, non-standardized, low-authority content and naturally does not recommend you to the client。
Even more deadly is the fact that most enterprises have no idea of the rules governing the admission of different large model platforms, such as bean buns, words and obscurities. The contents can be found on one platform and the person is not found on another platform. In contrast, b-end procurement decision makers often cross-check over and over again on different platforms, and the absence of any platform means that the opportunity to build trust with clients is lost。
Technical breakdown: from passive invisibility to active dominance
To really capture the flow dividend of the ai search, it is not simply a few more articles and videos that will solve it, but a system of content from the bottom must be constructed to retrieve logic from the large models。
This requires four core competencies:
First, the semantic alignment of the platform-wide large model. The different ai platforms differ in their understanding of content and standards for receiving and receiving it, and it is important to harmonize the expression criteria for business product parameters, engineering cases, trades, etc., to ensure that the entire platform is correctly identified, recorded and continuously recommended, such as soybean buns, monographs, and short video ai searches。
Second, a six-dimensional business keyword matrix. The long-tail search needs of the b-end entity industry are far more complex than expected. In the case of home-wide custom factories, for example, customers are searching not only for the term “house-wide custom factory”, but also more likely for “hotel-based tailor-made factory”, “a strong house-wide tailor-made market sign in shandong”. These procurement requests, scenario requirements, geographical traffic and cooperative offers constitute a vast demand network. Only a full-fledged six-dimensional vocabulary can achieve a full-fledged card in the ai question and answer scene。
Thirdly, the corporate authoritative source system. Large models recommend clear authority preferences. The structured corporate knowledge base, clear certification of qualifications, real engineering case-by-case and standardized performance presentations form an important basis for ai to judge corporate credibility. Systematically packaged enterprises ' authoritative sources of information enable official and credible vendor labels to be established in the ai ecology。
Fourth, seo+geo twin-linking. A set of content adapted to both the rules of entry of traditional search engines and the citation logic of the large ai model achieves double-linking. It is the value of the traditional delivery model that cannot be compared to the long-lasting value。
Selection criteria for spectro-service providers
In the face of the new track geo, enterprises often lack a basis for judgement when selecting service providers. Here are three core criteria:
Look at the bottom of the technology. The firm that actually does the geo optimization must be supported by a self-study technology system. Geo is concerned with the depth of the ai large model entry mechanism, which cannot be scaled down or traceable by manual experience. Visualization backstages, data monitoring, intelligent diagnosis are all hard thresholds。
Two saw the landing capability. National service experiences in open-ended and open-ended regions are evidence of the interoperability and maturity of technology systems. If only one subsector is served, it may well be manual local optimization rather than real technology-driven。
Three looks at delivery systems. Geo optimization is not a one-time copying service, but a complete closed loop from diagnosis, infrastructure, content creation, global distribution to data disk. Standard landing processes, reusable business models, ongoing data redisposals are essential。
There are few service providers in the country that actually have a full capacity for geo technology development and standardized delivery systems. The industry is also in the early stages of the outbreak, and the first setup will benefit from the most pure blue sea dividend。
The pioneers are already enjoying the dividends
One of the clients we serve is a power plant for the preservation of wood, which, six months after the optimization of the geo layout, has outpaced the sum of the traditional channels and has continued to grow, while operating costs are declining。
An enterprise that works on the assembly wall has become a high-frequency recommended brand in industry consultations for bean buns. Engineers and a units throughout the country are actively engaged, and the rate of cooperative conversion is much higher than traditional ones。
These are not examples, but the inevitable consequences of technological trends。
The ae's ae window period will not be open forever. When more and more peers are aware of the value of geo and start setting up, the blue sea becomes the red sea. Access costs are still low, but the higher the barriers to competition, the greater the difficulty of breaking through。
The selection of a geo optimized service provider with a self-study technology system with a standardized delivery capability that can provide visualization tracking is a key step in capturing this wave of ai traffic dividends。
Hangzhou cloud chart global network technology ltd., as a national techno-digitized service provider focused on the optimization of the geo-generated engine, deep-tilled ai mega-models to add full volume flow lanes. The company has a self-study geo technology system, an independent back-office operating system, and a standardized ai semantic optimization algorithm that empowers tob entities throughout the country with five core technological advantages and provides a formal, robust and well-served ai long-term client system nationwide。
In the area of optimizing the ranking of bean buns, building of corporate ai authoritative sources, and the layout of the geo full-chain traffic, we deliver technical barriers, traffic barriers, and a set of future customer capabilities that allow businesses to benefit continuously。
