How does Volcengine break down the "walls" for large model implementation by enabling AI to understand industries?
In 2025, the roar of large models accelerating in the industrial sector is piercing through the walls of technology.
This transformation, which started with technology and thrived in scenarios, is reshaping the digital landscape of all industries. It can be said that delving deeper into industrialization is the most practical proposition for artificial intelligence at present.
Currently, large models are integrating into all industries with unprecedented depth and breadth, and the wave of intelligent transformation is in full swing: from personalized services in the financial industry to precise reach in marketing scenarios, from intelligent interaction of hardware terminals to customized learning in education and scientific research.
Enterprises are investing resources to embrace AI. As large models are no longer an unattainable "luxury," a core question has emerged: Who is supporting the "last mile" of enterprise-level AI implementation?
Large model industrialization: Going deeper into the ecosystem
Since the upsurge of large models, all industries have been actively exploring how to integrate large models to drive intelligent transformation.
As early as 2024, the application of large models gradually moved from the early exploration stage to large-scale implementation. A significant sign was that major domestic cloud service providers "acted collectively" at the beginning of 2024, providing enterprises with one-click deployment and deep integration capabilities of large models, significantly lowering the threshold for large model commercialization.
The report "Analysis of the Market Landscape of China's Public Cloud Large Model Services, 1Q25" released by IDC shows that the number of large model calls on China's public cloud reached 114.2 trillion tokens in 2024. As domestic large models attracted global attention at the beginning of 2025, the demand for multimodal models and AI Agents was stimulated, and the daily call volume continued to grow at a high speed. Among them, Volcengine ranked first in the Chinese market with a market share of 46.4%, exceeding the sum of the market shares of the second and third places, showing a cliff-like leading growth trend.
Behind the phenomenal wave, there is a consensus in the field of large models: the industrial implementation of large models has become a reality. In the first half of this year, the implementation of large models also showed three trends:
First, the deepening of scenarios, and the value has been released from office efficiency to the core links of the industry.
Currently, large models are breaking through the enterprise office scenario and penetrating into the core businesses of industries such as finance, automotive, and technology. These high-value-density industries have taken the lead in transforming large models from simple "question-answering assistants" into the foundation for business innovation.
For example, many banks are currently building personalized content communities through large models. The automotive industry is applying the capabilities of large models to different experiences and conversion means such as intelligent cockpits, cross-terminal virtual assistants, and intelligent marketing. Industries with weak digital foundations such as manufacturing and energy are also accelerating the introduction of large models to optimize production processes.
Second, enterprises have changed from "passive innovation" to actively seeking implementation points.
In the past, enterprises more often passively tried to deploy large models due to technological anxiety or competitive pressure, mainly focusing on lightweight verification in marginal scenarios. They were also cautious in investment with vague goals. In essence, it was a "passive integration for fear of falling behind."
However, since this year, many enterprises have actively "developed" implementation points based on clear business pain points to solve practical problems. This also makes large models and cloud providers not only product service providers but also assume the roles of "business companions" and consultants in the current AI exploration.
The internalization ability of technology for industry cognition has also become the key to the large-scale implementation of large models.
Third, ecological collaboration continues to strengthen, and cloud providers have become the key promoters of large model implementation.
In the proposition of large model implementation, in addition to large model providers and enterprises, the co-construction of an ecosystem by multiple parties such as cloud providers, AI service providers, and application developers has changed from an option to a "must."
Among them, cloud providers solve the "last mile" problem of implementation. They are transforming from computing power providers to the cornerstone of industrial upgrading, and at the same time solving pain points such as high implementation costs and multi-model collaboration. For example, Volcengine, the cloud and AI service platform under ByteDance, while providing multimodal models, also provides basic services for building multi-cloud and multi-model environments, an Agent development platform, and a full-stack toolchain to help enterprises efficiently build and deploy AI applications.
Accelerating implementation: Multiple sectors including finance, automotive, mobile phones, consumption, and education are booming
To date, the industrial implementation of large models is no longer a concept that needs to be verified. It is accelerating in full swing in all industries:
So far, the Doubao large model has been widely implemented in industries such as automotive, intelligent terminals, the Internet, finance, education and scientific research, and retail consumption, covering 400 million terminal devices, 80% of mainstream automotive enterprises, 70% of systemically important banks, dozens of securities and fund companies, nearly 70% of top C9 universities, and more than 100 scientific research institutions.
For example, in the financial field, the application of large models enables every ordinary investor to make more "professional" investment decisions.
For ordinary investors, the inability to accurately and reasonably evaluate the returns of various financial products has always been a pain point that distinguishes them from professional investors and investment experts. At this time, if there is a financial product that can analyze the hotspots in the investment market for investors and help them make more reasonable judgments, it would be very suitable for ordinary investors. The cooperation between Guosen Securities and Volcengine just fills this market gap.
By summarizing and precipitating the investment research thinking of more than 3,000 professional investment advisors, Guosen Securities has created the Guosen Stock Market Assistant intelligent agent. Based on the Doubao large model, Volcengine's intelligent agent construction platform, and the Data Agent architecture, this intelligent agent can deeply empower various investment scenarios such as hot topic analysis, industry research, and financial knowledge Q&A.
When ordinary investors are faced with complex market information, it can capture financial news from the entire network in real-time, intelligently extract more than one million in-depth research reports, and link millions of knowledge entries. Then, through the MCP multi-source information processing center, it can track and analyze the popularity trends of nearly one billion financial short videos, intelligently identify the key events and emotional trends that truly drive the market, effectively filter out noise, and provide ordinary investors with accurate information and opinion analysis.
Relying on the engineering architecture and the deep thinking ability of large models, the Guosen Stock Market Assistant can also accurately switch between the "quick question and answer" and "deliberate thinking" modes like a real professional investment advisor, enabling ordinary investors to quickly understand key information while obtaining clearer and more logical analysis ideas.
In addition, the assistant has also deeply constructed an intelligent agent security protection system, building a three-dimensional protection barrier for the underlying model and upper-layer applications. Through multiple encryption mechanisms, real-time risk monitoring, and dynamic defense strategies, it makes investment behavior more secure and trustworthy.
In the automotive industry, the directions for large model implementation are more diversified.
After the human-vehicle interaction scenario entered the application era, problems such as lack of intelligence, lack of real-time performance, inability to achieve multi-terminal collaboration, and lack of understanding of users have emerged.
In the proposition of how to reshape the "human-vehicle relationship" through large models, each automotive enterprise has given its own answer. For example, SAIC Volkswagen, Mercedes-Benz, and BMW almost cooperated with Volcengine at the same time but with different application directions.
A few months ago, SAIC Volkswagen achieved in-depth cooperation with Volcengine in AI automotive co-creation. Based on the Doubao large model, they carried out comprehensive co-creation in areas such as intelligent cockpit innovation, in-vehicle content ecosystem, and enterprise digital efficiency improvement. Currently, its sub-brand SAIC Audi has co-created the Audi Assistant App with Volcengine based on the Doubao large model, achieving a breakthrough in cross-terminal interaction capabilities. This also means that after getting out of the car, the owner can continue the unfinished conversation with the Audi Assistant through the mobile phone, realizing continuous companionship across scenarios.
Mercedes-Benz has implemented large models into the competitiveness of new products, taking the lead in implementing the capabilities of large models into the new all-electric long-wheelbase CLA model. After this model is connected to the Doubao large model, the intelligent human-machine interaction system can recognize and respond to the multi-dimensional emotional states of the owner. Through the communication of emotional needs, the virtual assistant can more carefully link vehicle functions, continuously evolving the in-vehicle experience.
BMW's cooperation with Volcengine is more focused on the field of intelligent marketing. The two parties have created an AI-based marketing tool, aiming to shorten the customer's car purchase decision-making process.
Currently, the Doubao large model has covered 80% of mainstream automotive brands. It can be seen that intelligent vehicles integrated with large models are reshaping the core competitiveness of the automotive industry.
In the education and scientific research industry, the implementation fulcrum of large models lies in releasing the personalized creativity of teaching through the efficiency improvement of collaborative management and providing more convenient campus services.
Nankai University, as a leading institution in educational intelligence, launched the "Digital Nankai" project in 2023 and is currently co-building a benchmark case of "AI + education" in China with Volcengine. Volcengine has provided Nankai University with underlying capabilities from aspects such as infrastructure construction, model services, data governance, and scenario services, and has opened a large model application development platform across the university, enabling teachers and students to independently develop and apply AI capabilities in different sectors such as daily teaching, scientific research data processing, management, and services, allowing innovative awareness and achievements to emerge during the campus education stage.
At Zhejiang University, Volcengine has built the "Zhejiang University Gentleman" large model application system. Among them, the AI Scientist, as a one-stop scientific research intelligent platform in the "Zhejiang University Gentleman" system, integrates functions such as data integration, literature sorting, scientific research information acquisition, and writing assistance. Through large models, it provides users with support for multi-disciplinary, multi-lingual, and multi-modal data processing, helping to efficiently complete topic analysis, information search, trend judgment, and content generation, comprehensively improving scientific research efficiency and quality.
Similarly, Tongji University relied on Volcengine's HiAgent platform to build a campus AI application innovation platform and created a dedicated intelligent assistant, "Tongji Classmate." Based on this platform, Tongji University held the "Tongxin Cloud" AI application design competition, promoting the independent development of AI capabilities and scenario implementation. Judging from the actual results, the implementation of "Tongji Classmate" has not only achieved intelligent interconnection among multiple campus systems but also provided more convenient and efficient services for all teachers and students on campus.
In the field of intelligent terminals, as key scenarios for manufacturers' intelligent transformation such as voice assistants and AI search, how to achieve the safe, efficient deployment and real-time response of large models has become the core demand of the industry.
In the AI era, the "safe and imperceptible" engineering capabilities are almost the unique advantage of cloud service providers. For Lenovo, whether in the personal cloud or enterprise deployment scenarios, in the collaborative computing between the terminal and the cloud, the data needs to ensure "absolute security" while making large model inference faster.
In this regard, Volcengine's Jeddak AICC Confidential Computing Platform and Lenovo have achieved breakthroughs at two levels.
First, ensure the privacy and security of large models through end-to-end encryption. 100% end-to-end encrypted computing creates a "safe house" for users. Through the built-in hardware privacy protection mechanism, the user's input prompt can be transmitted in a fully encrypted environment. At the same time, through the transparent self-certification mechanism, it ensures that developers can verify the security of the computing process, fundamentally solving the risk of private data leakage in end-cloud collaboration.
Second, achieve both security and performance. In the privacy-encrypted environment, the inference speed of large models and the intelligent effect of the terminal are not compromised. Relying on the in-depth technical optimization of both parties, the end-to-end user-perceived latency of Jeddak AICC in the encrypted mode is close to that in the plaintext mode, and the inference efficiency of large models is almost undamaged. Terminal users can still obtain fast and accurate intelligent responses consistent with the plaintext environment while enjoying security protection.
In the consumer retail field, the goal of large models is to make the experience and service of digital consumption "more warm."
For example, with the help of the Doubao large model, Luckin has developed an intelligent agent equipped with an intention recognition and slot extraction engine, which can accurately predict and respond to consumers' ordering needs in real-time based on historical order data. During peak order periods, Volcengine's resource guarantee and performance stress testing support solutions enable the "Intelligent Coffee Butler" to obtain stable and sufficient computing resources, bringing a smooth ordering experience.
