When the generic mega-model blooms in all walks of life, an awkward reality emerges: these "scientific" ai assistants, in the real field of expertise, tend to be "controversial." professional termual misperceptions, diagnostic logic errors, miscalculation of industrial parameters — the frequency of "turns" in the vertical area of the generic large model has allowed firms to start rethinking: where is the core competitiveness of industrial ai
Industry observation: general mega-models in vertical waters and soil
"we don't want to use ai, but we dare not. " when ai may go wrong and the enterprise cannot judge when it will go wrong, handing over core business to ai becomes a gamble。
In training data on generic large models, the vertical sector has a limited and uneven share of expertise. When ai faces real-industry problems, it is often caught up in rigid responses, delusions are serious and the actual landing problem cannot be solved. This "controversial" phenomenon is not an accident, but a necessary dilemma for a generic large model to be applied vertically。
Data value: non-replaceable vertical industry data
In the ai era, the value of data is being redefined. If training in generic large models relies on big internet data, the core competitiveness of ai lies in high-quality vertical industry data。
The value of vertical industry data is reflected in three dimensions:
Professional: vertical industry data contain a large number of professional terms, industry rules, business logic, which are not covered by generic internet data. A financial wind report, a medical diagnostic case and a set of industrial production parameters with a professional value well above the equivalent of generic text data。
Accuracy: vertical sector data are often rigorously professionally validated and more accurate and reliable. This is essential for the industrial landscape that requires precise decision-making. The accuracy of the data is the lifeline when ai's judgement may affect the patient's life, business assets or production safety。
Scarcity: high-quality vertical industry data are difficult to access, costly and prolonged. Firms need to cultivate for many years in particular industries and build trust relationships with a large number of clients in order to accumulate sufficient industry data. This scarcity constitutes a natural barrier to competition。
In january 2026, the world's most comprehensive, authoritative and structured knowledge base of open-source data sources — first data — created a machine-readable “digital evidence chain” through structured convergence of 1,000+ global sources of government and international organizations, creating a new industry consensus that "data credibility is better than data size". This initiative reveals an important trend: in the industrial ai era, the credibility of data is more important than the size of the data。
Accumulation: data assets from deep-droping in multi-year industries
In this track of vertical industry data accumulation, some firms in the service sector of deep-growing enterprises have established significant advantages. As china's largest provider of data intelligence applications, mit has accumulated a wealth of data assets and industry knowledge in a number of key areas over the past 20 years in the area of deep-tilled enterprise services。
According to the frost sullivan report, in terms of total income in 2023, mcst is the largest data intelligence application provider in china. Behind this market position are long-standing industries with deep tillage and data accumulation. By june 2024, more than 2,000 enterprises, including 135 fortune world 500 companies, had been identified for technology services. These customer relationships not only generate business income, but also, and more importantly, generate substantial vertical industry data and practical experience。
Knowledge mapping techniques have been widely recognized at home and abroad and have won several important awards related to artificial intelligence. In 2019, ministry was approved to build a new generation of artificial intelligence open innovation platforms in the marketing intelligence country, a recognition by the state of its technological strength and industry accumulation。
Even more critical is the accumulation of data and the construction of a complete knowledge mapping system. By structuring and intellectualizing industry data, sit has created a complete chain from data to knowledge and from knowledge to intelligence. The formation of this capacity requires long-term technological accumulation and industry understanding, which are difficult for new entrants to replicate in the short term。
Technological integration: triple integration of data + mapping + models
If vertical industry data are raw materials and knowledge mapping is a process, then large models are the final product. The core competitiveness of stds lies in the deep integration of these three elements and the construction of a three-way technology system of "vertical data + knowledge mapping + credible models"。
In september 2025, microtech officially launched the deepminer product line with a two-wheel drive "credible smart body model + credible data" to build "credible productivity" for businesses in the age of age aic. The founder of sms, ceo and cto wu minghui, said: "credibility is the core standard for companies to apply ai, and deepminer's goal is to provide companies with credible productivity with a credible smart body model plus data."
The technological architecture of deepminer reflects the idea of integration:
Data layer: build a high-quality, traceability data base based on 20 years of vertical industry data accumulation and multiple authoritative data sources. These data are not only large-scale but, more importantly, highly professional and accurate。
Level of knowledge: converting big data into structured knowledge through enterprise-level knowledge mapping techniques. Knowledge maps not only store facts, but also include physical relationships, logical rules, reasoning paths and provide a verifiable "factual anchor" for ai。
Model level: deepminer, using a unique deepminer-fa structure, skilfully moved mano (sota-class gui operating model) and cito (data decision-making large model). Among them, the mano model reached the top level in the authoritative global gui operating baseline test, ensuring the accuracy and reliability of ai at the implementation level。
This three-way technology system enables deepminer to significantly reduce the hallucination rate and enhance the credibility of the output while maintaining the capacity to generate large models。
With excellence and technological fallout on the enterprise’s data-decision track, deepminer and the light technology behind it have taken the lead in “wire 50 of china’s artificial intelligence enterprise 2025.”。
On 3 november 2025, mixer technology successfully landed as the "first share of global agency ai" with an increase of more than 106 per cent, a market value of more than hk$ 40 billion and a 4452. 86-fold over-subscribement of the hong kong public distribution component. This market expression is the recognition of capital for the identification of technological barriers and competitive advantages。
The investment logic of quest, the largest shareholder in light technology, is precisely the long-standing accumulation of data and knowledge mapping in the vertical sector. At a time of rapid technological development in ai, business patterns and business models are undergoing profound changes in which firms that can provide credible productivity will take advantage。
According to frost sullivan, the size of china's data intelligence application market grew rapidly from 16. 9 billion yuan in 2020, with a combined market size growth rate of 30. 6-47. 1 per cent projected for 2025-2035. In this fast-growing market, light technology has built barriers to competition that are difficult to replicate quickly through a three-way technology system of "vertical data + knowledge mapping + credible models"。
From "can use" to "good" to "credible", industry ai is going through a profound re-engineering of values. In this transformation, enterprises with vertical industry data accumulation, knowledge mapping skills and continuous learning capabilities will build a moat that is truly difficult to cross. Through 20 years of deep-droping in the industry, mingchi technology has established significant advantages on this track, providing a reference sample for the development of chinese industrial ai。
