Four Pillars · KHB AI Super Brain Head Plan

The Foundational Logic of the Four Pillars

Building industrial AI is not a single-dimensional endeavor—it's a systematic project. If you only have computing power without industry knowledge, you have "brawn without brains." If you only have knowledge without computing power support, you have "brains without brawn." If you have both computing power and knowledge but no standards, you cannot achieve scaled industrial impact. And if you have all three but no ecosystem, you cannot maximize value or achieve sustainable development.

The four pillars of the KHB AI Super Brain Head Plan—Computing Co-Build, Knowledge Co-Build, Standard Co-Build, and Ecosystem Co-Build—are precisely designed based on this deep thinking. They are not four isolated modules, but an organic whole: computing is the foundation, knowledge is the core, standards are the bridge, and ecosystem is the amplifier. All four are indispensable, together forming a complete closed loop of industrial AI infrastructure.

Design Philosophy: Starting from the computing base, building upward the knowledge layer, extending outward the standards layer, and ultimately forming an ecosystem network. Each layer provides support for the layer above it, and each layer becomes more valuable because of the layer below it.

🏗️ Why Start with Computing

Computing power is the "electricity" of the AI era and the foundation of all AI applications. Without stable, sufficient, and low-cost computing power, any AI model and application would be a castle in the air. KHB possesses scaled computing infrastructure—this is our starting point and our "opening gift" for industry partners.

🧠 Why Knowledge Is Core

The capability boundary of general large models has emerged; real industrial value lies in industry-specific knowledge. The massive business data, expert experience, and industry know-how accumulated by leading enterprises are the core assets for AI implementation. Only by structuring and modeling this knowledge can true industry barriers be created.

📋 Why Standards Are Key

Without standards, each enterprise operates independently, AI service quality varies, customer selection costs are high, and the industry struggles to scale. By establishing industry AI service standards, we can build industry trust, reduce transaction costs, form network effects, and ultimately benefit the entire industry.

🌐 Why Ecosystem Is the Amplifier

The value of a single joint venture is limited, but if an ecosystem network can be formed, value grows exponentially. Through application matrix, channel network, industrial fund, and exit mechanism, more participants can join in to collectively grow the industrial AI pie.

Pillar One: Computing Co-Build

01

COMPUTING CO-BUILD

Computing Co-Build

KHB invests computing infrastructure, partners invest industry scenarios, together building industry-exclusive computing bases for optimal resource allocation and efficient utilization.

Server Clusters

The core of computing co-building is server clusters. KHB possesses scaled GPU/CPU computing clusters covering the full range of scenarios from training to inference. Our server clusters use industry-leading hardware configurations, including the latest generation GPU accelerators, high-speed NVMe storage, and low-latency network switching equipment, capable of meeting the demands of various AI workloads such as large model training, fine-tuning, and inference.

In the joint venture, KHB configures dedicated computing resource pools based on industry characteristics and business needs. Unlike the shared model of public clouds, dedicated computing pools provide higher performance stability and data security. For industries with special data compliance requirements, we can also provide physically isolated dedicated clusters to ensure data stays within domain and models remain secure.

Training Clusters

High-bandwidth, large-memory GPU clusters supporting kilocalorie-scale distributed training with high-speed interconnect networks and parallel training frameworks.

Inference Clusters

Low-latency, high-throughput inference service clusters supporting elastic scaling, on-demand allocation, and optimal cost efficiency.

Storage Clusters

Distributed high-speed storage systems supporting tiered hot/cold data storage for massive training data and model file storage needs.

Data Centers & Bandwidth

Computing power cannot exist without data center and network support. KHB possesses T3+-grade data center resources at multiple core nodes nationwide, with comprehensive power protection, cooling systems, and security防护体系. Each data center adopts a three-tier power supply scheme of dual utility power + UPS + diesel generators, with availability exceeding 99.99%.

On the network side, we have established deep partnerships with all three major telecom operators, with abundant bandwidth resources and BGP multi-line access capabilities. Data centers are interconnected through high-speed dedicated lines, forming a nationwide computing network that allows flexible scheduling of computing resources according to business needs. For scenarios with low-latency requirements, we can also deploy edge computing nodes to extend computing power closer to users.

Computing Scheduling

Having hardware resources is not enough—an efficient scheduling system is needed to maximize value. KHB independently developed an intelligent computing scheduling platform that automatically allocates optimal computing resources based on multi-dimensional factors such as task type, priority, and cost. The platform supports hybrid cloud architecture and can uniformly manage self-owned and cloud computing resources for elastic scaling.

The core capabilities of the scheduling platform include: container-based resource isolation, second-level elastic scaling, intelligent task queuing and priority scheduling, and refined cost accounting and usage statistics. Through this system, we can increase computing utilization by over 30% while significantly reducing unit computing costs. For the joint venture, this means lower operating costs and faster business response times.

Operations Team

Stable operation of computing infrastructure cannot be separated from a professional operations team. KHB has an experienced operations team, with members mostly coming from leading internet companies and cloud service providers, possessing comprehensive capabilities in large-scale data center operations, network operations, system operations, and security operations. The team provides 7×24 monitoring and operations support to ensure stable operation of the computing platform.

In addition to traditional operations work, our team also provides AI engineering support, including professional services such as model deployment optimization, inference performance tuning, and training efficiency improvement. Many industry partners have great algorithm ideas but lack engineering implementation capabilities. Our team can help them quickly turn algorithm prototypes into production-grade AI services, significantly shortening time-to-market.

Computing Valuation Model

In joint venture cooperation, how to value computing power is a key issue. KHB adopts an innovative model of "computing as equity + tiered pricing." In the initial stage, KHB invests a certain scale of computing infrastructure into the joint venture company in exchange for corresponding equity ratio, demonstrating our sincerity in cooperation. As the business develops, computing usage exceeding the initial investment is charged to the joint venture at preferential prices below market rates.

The benefits of this model are obvious: first, it lowers the partner's upfront investment threshold, eliminating the need to purchase a large number of servers at once; second, KHB's interests are deeply tied to the joint venture company, and we will fully support its development; third, the tiered pricing mechanism ensures that unit costs continue to decrease as usage increases, with economies of scale. At the same time, computing costs are transparent and controllable, and the joint venture can clearly calculate business costs and profits.

Pillar Two: Knowledge Co-Build

02

KNOWLEDGE CO-BUILD

Knowledge Co-Build

Partners contribute industry data and expert experience, KHB contributes AI technology and engineering capabilities, together building industry-exclusive large models and knowledge bases, forming irreplicable knowledge barriers.

Industry Data

Data is the fuel of AI, and industry data is the core fuel of industry AI. Leading industry enterprises have accumulated massive amounts of business data over years of operation, including customer data, transaction data, service records, documentation, and more. This data contains rich industry knowledge and business patterns, and is a valuable raw material for training industry-exclusive large models.

In the knowledge co-building process, we first conduct a comprehensive inventory and sorting of the partner's existing data to identify valuable data assets. Then, based on the type and characteristics of the data, we design corresponding data processing and annotation schemes. For structured data, we perform cleaning, deduplication, and normalization; for unstructured data (such as documents, images, audio, video), we use multimodal understanding technology for structured extraction. All data processing is conducted in a secure and controllable environment, strictly protecting data privacy and business secrets.

Business Data

Customer information, transaction records, service tickets, financial data and other structured business data are the basic carriers of industry knowledge.

Document Knowledge

Industry reports, technical documents, policies and regulations, training materials and other unstructured documents are important deposits of expert knowledge.

Behavior Data

User behavior, operation logs, service processes and other process data reflect patterns in real business scenarios.

Knowledge Graph

Raw data alone is not enough—data needs to be transformed into structured knowledge. Knowledge graphs are the key technology for achieving this transformation. Based on industry data, we build industry knowledge graphs that structurally organize knowledge elements such as entities, concepts, relationships, and attributes to form machine-understandable industry knowledge bases.

Knowledge graph construction is a continuous iterative process. Initially, we use NLP technology to automatically extract knowledge entities and relationships from existing documents and data, forming the skeleton of the knowledge graph. Then, through manual review and expert annotation, we continuously expand and optimize the knowledge graph content. Knowledge graphs not only provide precise knowledge enhancement for large models, but can also be directly used in application scenarios such as intelligent Q&A, intelligent search, and intelligent recommendation, improving the accuracy and explainability of AI services.

Expert Annotation

High-quality annotated data is a prerequisite for training excellent AI models. Industry knowledge is highly professional, and general annotation teams struggle to handle it competently. We have established an annotation system of "AI pre-annotation + expert review + continuous iteration," fully leveraging the experience and wisdom of industry experts.

Specifically, KHB's algorithm team first uses pre-trained models to pre-annotate data, generating preliminary results. Then, industry experts from the partner side conduct review and correction to ensure the professional accuracy of annotation results. Experts are not just annotating data—they are "feeding" their years of accumulated experience and judgment into the AI system. Through this process, the tacit knowledge of experts is transformed into explicit knowledge that AI can understand and apply. At the same time, we have established a complete annotation quality control system, ensuring high-quality annotated data through multi-expert cross-validation, consistency testing, and other methods.

Model Fine-Tuning

With high-quality industry data and knowledge, the next step is industry-specific fine-tuning based on general large models. KHB has a mature large model fine-tuning technology stack, supporting full-parameter fine-tuning, LoRA fine-tuning, QLoRA fine-tuning and other fine-tuning methods, allowing selection of the optimal fine-tuning strategy based on model scale and data volume.

Fine-tuning is not simply "feeding data"—it's a systematic project. We design specialized fine-tuning schemes based on industry characteristics and application scenarios, including multiple aspects such as instruction dataset construction, dialogue style customization, domain knowledge injection, and safety alignment. The fine-tuned industry large model will significantly improve performance in professional domains while maintaining the language understanding and generation capabilities of general large models. In addition to fine-tuning, we also conduct extensive engineering optimization, including model quantization, inference acceleration, deployment optimization, etc., ensuring efficient model operation in production environments.

IP & Benefit Distribution

Knowledge co-building involves the ownership and benefit distribution of intellectual property rights, which is a very sensitive and important aspect of cooperation. Our principle is "respect contributions, clear property rights, shared benefits."

Specifically, the intellectual property rights of base large models and general technologies belong to KHB—this is the result of our years of technology accumulation. On this basis, derivative intellectual property rights such as industry-exclusive models, knowledge bases, and knowledge graphs generated through industry data and knowledge fine-tuning belong to the joint venture company. Both parties enjoy corresponding rights and interests according to their equity ratios. At the same time, we strictly protect the ownership of raw data and trade secrets provided by the partner, using them only within the scope of cooperation and not for other purposes. This arrangement protects the core interests of both parties and lays a solid asset foundation for the long-term development of the joint venture.

Pillar Three: Standard Co-Build

03

STANDARD CO-BUILD

Standard Co-Build

Jointly developing industry AI service standards with industry leaders, research institutions, and regulatory authorities to build industry trust systems and seize industrial discourse power.

Service Standards

Standardization of AI services is a prerequisite for large-scale industry development. Without standards, each service provider says different things, customers don't know how to choose, and they don't know how to evaluate service quality. We are committed to establishing a complete industry AI service standard system covering all aspects including service content, service quality, service processes, and pricing models.

Service standard development follows the principles of "practicality, science, and openness." Practical means standards should fit actual industry needs and truly solve industry pain points; scientific means standard development is based on extensive data and practical verification, with sufficient technical basis; openness means the standard development process is open to the industry, welcoming more enterprises to participate and contribute. By establishing service standards, we can reduce industry transaction costs, improve overall service levels, and promote the industry from "wild growth" to "standardized development."

Quality Standards

Define core indicators of AI services such as accuracy, response speed, and availability, establishing a quantifiable service quality assessment system.

Process Standards

Standardize delivery, operation, and upgrade processes of AI services to ensure consistency and predictability of services.

Interface Standards

Develop unified API interface specifications and data format standards to reduce system integration costs and promote ecosystem interoperability.

Evaluation System

After standards are developed, how to ensure execution and implementation? This requires establishing a scientific evaluation system. We have established a comprehensive evaluation system covering multiple dimensions including technical evaluation, business evaluation, and security evaluation, conducting comprehensive inspection and certification of AI services.

Technical evaluation mainly focuses on model performance indicators, including accuracy, recall rate, response time, concurrency capability, etc. Business evaluation mainly focuses on the actual value of AI services to the business, including efficiency improvement, cost reduction, revenue growth, etc. Security evaluation focuses on aspects such as data security, model security, and content security, ensuring the compliance and credibility of AI services. Through this evaluation system, AI services can be objectively and fairly rated, providing a reference for customers to choose services, and also setting quality benchmarks for the industry.

Compliance Framework

AI compliance is the bottom line of industry development. With the introduction of regulations such as the "Interim Measures for the Management of Generative AI Services," compliance requirements for AI services are increasing. Different industries also have their own industry regulatory requirements, such as risk control requirements in the financial industry, privacy requirements in the medical industry, and professional norms in the legal industry.

In standard co-building, we attach great importance to the construction of compliance frameworks. We deeply study relevant national and industry laws and regulations, and combined with the characteristics of AI technology, have established a complete compliance framework. This framework covers multiple aspects including data compliance, algorithm compliance, content compliance, and ethical compliance, escorting the healthy development of AI services. At the same time, we maintain close communication with relevant regulatory authorities, actively participate in policy formulation and pilot work, and promote the improvement of industry compliance standards.

White Paper Publication

White papers are an important carrier of standard output and an important manifestation of industry influence. We plan to jointly publish industry AI development white papers regularly with industry partners and authoritative institutions, sharing industry insights, technology trends, best practices, and other content.

The content of white papers includes: current status and trends of industry AI development, industry AI technology roadmap, industry AI application scenario analysis, industry AI standard system, industry AI typical cases, etc. By publishing white papers, we can convey our professional voice to the entire industry, establish industry discourse power, and enhance the brand influence of the Head Plan. At the same time, white papers are also an important tool for attracting more partners to join, letting more enterprises understand our philosophy and methods, and jointly promoting the development of industry AI.

Industry Alliance

With the strength of one or a few enterprises alone, it is difficult to establish industry-recognized standards. It is necessary to unite multiple forces such as upstream and downstream enterprises in the industrial chain, scientific research institutions, and industry associations to form an industry alliance and jointly promote the formulation and promotion of standards.

Our vision is to take the joint venture enterprises of the Head Plan as the core to initiate the establishment of an industry AI industry alliance. The mission of the alliance is to promote the standardization, industrialization, and ecological development of industry AI. The main work of the alliance includes: formulating industry standards, conducting technical exchanges, organizing talent training, promoting policy recommendations, and promoting industrial cooperation. Through the alliance form, industry consensus can be凝聚, industry resources integrated, industry synergy formed, and together growing the industrial AI pie. For enterprises participating in the alliance, they can not only gain first-mover advantage and standard discourse power, but also expand business opportunities and network resources.

Pillar Four: Ecosystem Co-Build

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ECOSYSTEM CO-BUILD

Ecosystem Co-Build

Building a four-in-one industrial ecosystem of application matrix, channel network, joint ventures, and industrial funds, letting AI value flow and appreciate within a broader scope.

Application Matrix

Once industry AI infrastructure is built, rich applications are needed to release value. We have planned an application matrix covering full industry scenarios—from internal efficiency tools to customer service products, from professional solutions to general SaaS applications—forming a multi-level, three-dimensional application ecosystem.

Application matrix construction adopts a multi-wheel drive model of "self-developed + cooperation + investment." For core, high-frequency application scenarios, the joint venture company self-develops to ensure core competitiveness; for segmented, professional scenarios, through ISV cooperation, third-party developers are introduced to develop applications based on our base; for potential startup teams, investment incubation is conducted through industrial funds. This not only guarantees the competitiveness of core applications but also fully mobilizes the enthusiasm of ecosystem partners, forming a flourishing application ecosystem.

Efficiency Tools

Intelligent customer service, intelligent documents, intelligent search, intelligent analysis and other internal efficiency tools help enterprises reduce costs and increase efficiency.

Business Systems

AI applications deeply integrated into business processes, such as intelligent marketing, intelligent risk control, intelligent diagnosis, intelligent design, etc.

SaaS Products

Standardized SaaS products for industry customers, enabling rapid replication and promotion for scaled revenue.

Channel Network

Good products need good channels to reach customers. We will fully utilize the channel resources accumulated by partners in the industry, while actively expanding new channel partners, building a sales and service network covering the entire industry.

The channel network includes multiple forms such as direct sales channels, partner channels, and online channels. Direct sales channels mainly serve large customers, providing deep customized services; partner channels include industry agents, system integrators, consulting companies, etc., leveraging partner power to quickly cover a wider customer base; online channels sell and serve standardized products through internet platforms. We provide all-round support for channel partners, including product training, technical support, marketing support, business opportunity sharing, etc., growing together and sharing benefits with channel partners.

Joint Ventures

Joint venture companies are the core carriers of the Head Plan and also the core nodes of ecosystem co-building. Each industry track's joint venture is an independent operating entity with its own team, products, customers, and business model. As a strategic investor and technology enabler, KHB provides all-round support for joint ventures including computing power, technology, brand, and resources.

The governance structure of joint ventures is carefully designed, ensuring both the partner's dominance over industry business and KHB's strategic synergy and technology empowerment. At the board level, both parties appoint directors according to equity ratios, and major decisions are discussed together. At the management level, the partner's team leads, and KHB sends technical and operational experts to provide support. This "industry team leadership + KHB strategic empowerment" model both leverages the respective advantages of both parties and ensures the flexibility and execution of the joint venture.

Industrial Fund

Industrial funds are important tools for ecosystem building. We plan to jointly initiate and establish industrial AI investment funds with local governments, industrial capital, financial institutions, etc., investing in high-quality enterprises and startup projects upstream and downstream of the industrial chain.

The investment strategy of industrial funds revolves around "chain supplementation, chain strengthening, chain extension." Chain supplementation means investing in missing key links in the industrial chain to improve ecosystem layout; chain strengthening means investing in technologies and products that can enhance core competitiveness; chain extension means investing in emerging directions that can expand industrial chain boundaries. Through industrial fund investment, not only can financial returns be obtained, but more importantly, ecosystem synergy can be strengthened, forming a community of interests and promoting the prosperity and development of the entire industrial ecosystem.

Exit Mechanism

A healthy ecosystem needs clear exit paths, allowing early participants to realize value returns and new capital to continuously enter. We have planned diversified exit paths for joint ventures to ensure that the interests of all parties can be reasonably realized.

Exit paths include: independent IPO, which is the most ideal exit method and can maximize value; acquisition by listed companies, which for certain industries may be a more realistic choice; equity repurchase, where KHB or the partner can repurchase the other party's equity under specific conditions; equity transfer, transferring equity to third-party investors or industrial capital. Diversified exit mechanisms provide sufficient liquidity guarantees for investors and partners, and also provide flexible capital operation space for the long-term development of joint ventures.

The Synergy of the Four Pillars

The four pillars are not four isolated modules but an organic whole. They support and promote each other, forming a complete closed loop from infrastructure to industrial ecosystem. Only by understanding this synergy can one truly understand the value of the Head Plan.

INDUSTRIAL AI
INFRASTRUCTURE

🏗️

Computing

Foundation Base
Supporting Upper Layers

🧠

Knowledge

Core Value
Forming Barriers

📋

Standards

Bridge & Bond
Building Trust

🌐

Ecosystem

Value Amplifier
Exponential Growth

Computing & Knowledge Synergy

Computing power is the "container" and "engine" of knowledge. Without computing power, knowledge cannot be modeled and productized—it can only remain in experts' minds and paper documents. With computing power, massive industry data can be processed quickly, complex large models can be trained efficiently, and intelligent applications can be deployed at scale. Conversely, knowledge makes computing power more valuable. If you only have computing power without industry knowledge, computing power is just a pile of expensive hardware that cannot generate targeted industry value. Knowledge gives computing power a soul, enabling computing power to solve specific industry problems and create tangible business value.

Knowledge & Standards Synergy

Knowledge is the foundation of standards, and standards are the sublimation of knowledge. In the process of co-building industry knowledge, we have accumulated deep understanding and practical experience of the industry, all of which provide a solid content foundation for standard formulation. Conversely, the standard formulation process promotes the沉淀 and refinement of knowledge. To formulate standards, we need to systematize and make explicit scattered, tacit knowledge—this process itself is a sublimation of knowledge. Moreover, the promotion and application of standards will expose more enterprises to and use this knowledge, further verifying and improving the knowledge system.

Standards & Ecosystem Synergy

Standards are the "common language" of the ecosystem, and the ecosystem is the "amplifier" of standards. With unified standards, all participants in the ecosystem can communicate and collaborate smoothly, applications can interoperate, and data can flow freely. Without standards, the ecosystem is fragmented, with each participant acting independently and unable to form synergy. Conversely, ecosystem growth multiplies the value of standards. The more enterprises that use standards, the stronger the network effect of standards, and the greater the value of standards. At the same time, ecosystem feedback also drives continuous iteration and optimization of standards, making them more complete and mature.

Ecosystem & Computing Synergy

Computing power is the "infrastructure" of the ecosystem, and the ecosystem is the "source of demand" for computing power. Ecosystem development generates more computing power demand, driving continuous investment and construction of computing infrastructure. And the continuous improvement of computing infrastructure supports the birth and development of more innovative applications in the ecosystem. This positive cycle drives the entire industrial AI ecosystem to continuously evolve and upgrade. At the same time, scaled computing demand also brings cost reductions, making computing power more inclusive, allowing more SMEs to use high-quality AI services, further expanding ecosystem coverage.

Closed Loop Formed: Computing → Knowledge → Standards → Ecosystem → More computing demand—this is a continuously self-reinforcing positive cycle. When this cycle spins up, it creates a powerful flywheel effect that drives continuous evolution of industrial AI infrastructure and growing value.

The Four Pillars of the Head Plan