Original · Unique Research · 2025-10-31
Editor’s note: This is a full translation of the source article and interview published on October 31, 2025. Its evaluative language, customer relationships, performance figures, business-model descriptions and data-compliance assertions are retained as claims made by the original report or the interviewee, not independently audited findings or legal advice. The article describes a 3-day-to-20-minute manufacturing workflow separately from a 3-day-to-2-hour shipping workflow and a response-time metric of within 1 hour; these are preserved without treating them as a single benchmark. In Q12, the Chinese source literally says “replacing AI,” despite the surrounding emphasis on human–machine collaboration, and prints “80” without a percent sign or another unit in its final value expression. Both ambiguities are retained rather than silently corrected. No publication location is inferred.
The AI wave is surging in, and the market is awash with lofty talk about what sits ‘above the model.’ Yet amid all this noise, one company has chosen a different path. Instead of discussing ‘what AI can do,’ it is working pragmatically to answer a more immediate question: ‘What can AI actually accomplish for enterprises today?’
That company is YUHE.AI. Founded only two years ago, it has already charted a clear trajectory in the high-value, highly specialized field of ‘digital pre-sales employees.’ That trajectory does not depend on flashy packaging or concepts. It rests on a simple principle: AI is not meant to replace people, but to unlock human potential; it is not merely a tool, but a partner that can fight alongside you.
AI Is Not Software, but a “Job Capability Model”
YUHE.AI’s product is neither an App nor a collection of model APIs. It is a complete set of digital employees that can ‘start work immediately.’ The first role they serve is one of the most undervalued yet valuable positions in B2B transactions: pre-sales.
In heavy industries such as manufacturing, telecommunications, and shipping, pre-sales can determine whether a commercial battle is won. Customers may struggle to articulate their needs, suppliers’ capabilities can be difficult to assess, and a high-quality proposal becomes a litmus test for trust. Yet this role has long been chronically understaffed because people with both technical and marketing expertise are scarce. YUHE.AI addresses the problem with a Pre-Sales Agent that can automatically understand customer requirements, generate customized solutions and quotations, and even produce complete tender documents with one click.
This is not ‘AI assisting people at work’; it is ‘AI as the role itself.’ In the architecture YUHE.AI has built, every Agent is a collection of capabilities organized around a specific job function, complete with SOPs, industry knowledge graphs, and response strategies. It is like a ‘super intern’ who can perform from the first day on the job. The difference is that it needs no training, never changes employers, and never gets tired.
Outcome-Oriented, Without Making Money by ‘Selling Models’
Unlike AI companies that rely on subscription-based SaaS or compute licensing, YUHE.AI’s business model is a breath of fresh air in B2B AI. It does not charge by usage; instead, it shares in the business outcomes actually generated.
You can think of it as an AI employee on a ‘base salary plus commission’: if it helps you close an order or save labor, it earns a little ‘commission’; if it produces no tangible result, the enterprise does not have to pay an extra cent. This Results as a Service, or RaaS, model gives AI a genuine incentive to ‘take responsibility for performance’ for the first time, while compelling the team to keep refining the product around outcomes.
The confidence behind this model comes from the team’s substantive exploration of Agentic AI. Rather than being drawn in by the illusory promise of ‘general-purpose agents,’ it has committed to ‘specialized intelligence for vertical scenarios.’ From manufacturing pre-sales and logistics supply chains to premium retail, every Agent’s training data, behavioral model, and response logic are drawn from the accumulated experience and co-creation of real roles. AI is not a ‘universal solution,’ but a ‘precise solution.’
Agentic AI in Practice: Not a Concept, but Delivered Results
While the outside world is still debating whether Agentic AI has arrived, YUHE.AI has already delivered a report card in live projects.
For example, a quotation Agent it built for a centrally administered state-owned shipping group can complete in 2 hours a tender-response process that previously took 3 days, bring response time to within 1 hour, raise the repeat-purchase rate to 65%, and increase average customer order value by more than 20%. Behind these figures is the company’s redefinition of the ‘Agent’: not an upgraded Chatbot, but a ‘role operator’ that can understand business semantics, master the logic of a position, and close the loop across an entire process.
Its technology stack likewise does not stop at the ‘model plus interface’ level. It is an end-to-end architecture spanning perception—seeing accurately—cognition—finding accurately—and execution—doing accurately. Complex-document parsing, multimodal semantic understanding, high-precision RAG context management, and a role-level behavioral-logic engine are all technologies implemented in service of ‘outcome orientation,’ not displays of technical prowess.
Ignoring Hype to Focus on Creating an ‘Outcome Loop’
Zhai Xingji, the founder of YUHE.AI, does not speak with the grandiosity of an ‘AI evangelist.’ He is more like an ‘AI practitioner’: he rejects ineffective projects driven by KPI targets, avoids highly customized and complex engineering engagements, and would rather move a little more slowly if that ensures every Agent can keep creating value in real-world settings.
His vision is not grandiose; it is radically pragmatic: use AI to strengthen real enterprise operations so that every company can have its own “super employee.”
The company has not limited its sights to the domestic market. In Southeast Asia, Japan, and South Korea, it is gradually advancing product standardization and delivery efficiency through localization and channel partnerships. In Europe and North America, it is seeking interoperability with SaaS platforms in the hope of becoming a ‘missing-role provider’ within enterprise-system ecosystems.
The challenges it faces are equally clear: the complexity of cross-border compliance, the difficulty of adapting local data, and the impact of cultural differences on the acceptance of Agents. But the company does not intend to force its way through by ‘throwing money at the problem.’ Instead, it aims to find a path around those barriers with a focused, high-quality technology product and strong brand.
A Pioneer of the ‘Individual Agent’ Era: When One Person Becomes a Team
As AI reshapes social structures, the very idea of the ‘individual’ is also being redefined.
In the past, a pre-sales professional needed team support and accumulated specialist expertise to write tender proposals, design solutions, answer questions, and prepare quotations. Today, an ordinary salesperson can independently complete the entire process with YUHE.AI’s Agent.
Individuals no longer have to depend on organizations, processes, or back-office support. With AI-enabled capability outsourcing and process automation, everyone can become a ‘micro-enterprise.’ This is the embryonic form of a true ‘individual agent’ era.
For Zhai Xingji, this is not a showcase for technological bravado. It is a revolution in organizational structure and a profound reshaping of the relationship between people and machines.
Final Thoughts: AI Does Not Replace People; It Amplifies Them
YUHE.AI has taken an underestimated route. It did not build a chatbot or chase the trend of multimodal generation. Instead, it entered through the “job-specific agent,” the point with the clearest implementation path and strongest ROI potential. It has turned AI from a technology that feels visible yet out of reach into a system that can be engaged and held accountable for delivery.
If early AI represented a ‘capability upgrade,’ YUHE.AI represents an ‘organizational upgrade.’ If AGI is a futuristic vision that has yet to arrive, what the company is doing is turning AI into a ‘practical tool’ that can be used today.
This is an ‘outcome-oriented’ company, and it also embodies a ‘use it and move on’ philosophy of AI—taking pride not in technology itself, but in results. Perhaps this is the other kind of heroic narrative the AI era needs most: not a story about technology changing the world, but about how technology can first help one person accomplish something that was previously beyond reach.
Selected Interview Q&A
Q1: What is the core positioning of YUHE.AI, and what principal pain point does it solve for enterprises?
Zhai Xingji: YUHE.AI is a B2B AI-native company founded in May 2023. Our core positioning is to provide industries with out-of-the-box digital pre-sales employees.
The central pain point we address is the shortage of specialized sales—pre-sales—talent. In B2B business processes, pre-sales is a critical bottleneck affecting deal efficiency and revenue growth. The role requires an understanding of both technology and marketing, making such people scarce in the market and slow to develop. Our digital pre-sales employees are designed to solve this pain point and deliver directly measurable business outcomes to enterprises.
Q2: How specifically does your company’s ‘digital pre-sales employee’ product address the pain points of B2B enterprises?
Zhai Xingji: Our digital pre-sales employee, or Pre-Sales Agent, helps enterprises complete essential pre-sales tasks without relying on a large human sales force, closing the entire loop across technology, requirements, and solutions.
Its specific functions include:
Customer-requirement interpretation: Using natural-language processing to analyze customer requirements—such as tender documents—with precision.
Solution development: Automatically generating technically appropriate solutions and customized documents.
Tender-document production: Responding rapidly to tender requirements and generating standardized bids.
Product Q&A: Responding to and answering technical questions in real time to strengthen customer trust.
Quotation generation: Automatically producing compliant quotations based on requirements and shortening the decision cycle.
To date, we have helped leading companies including INESA, COSCO Shipping Heavy Industry, and Vanchip Technologies achieve business breakthroughs.
Q3: What is distinctive about YUHE.AI’s business model, and why does the company not use a traditional software-payment model?
Zhai Xingji: We use an outcome-oriented charging model that takes a share of actual business value, moving beyond the traditional model of paying for tools, such as SaaS subscriptions.
We take a percentage only after an enterprise has achieved real business value through our digital employees—for example, closing a contract or saving costs. The benefits are:
Lower risk of trial and error for customers: Customers do not need to prepay high fixed costs.
Aligned interests: Our revenue is directly tied to the customer’s business success, compelling us to deliver genuine business outcomes.
Fit for high-value scenarios: This model is particularly well suited to B2B fields with high transaction values and complex decision processes.
Q4: In terms of core technology, how does YUHE.AI ensure the professional competence of its digital employees—the ability to ‘see accurately, find accurately, and do accurately’?
Zhai Xingji: This reflects our core technological barrier and is based primarily on three independently developed technical directions:
Seeing accurately—multimodal parsing: We have advanced technology for parsing unstructured and multimodal content, allowing us to identify and understand complex documents—such as tender documents containing tables, charts, and formulas—with high precision. We optimize vertical scenarios through our self-developed small models, achieving both high accuracy and fast response speeds.
Finding accurately—high-precision RAG algorithms: We have implemented fine-grained context management in our RAG, or retrieval-augmented generation, system. We classify the business knowledge of different roles by feature and apply differentiated approaches to context management, effectively addressing the ‘context noise’ problem in general-purpose RAG and substantially improving retrieval precision.
Doing accurately—a vertical role Agent architecture: Our Agent architecture is co-created with customers and driven by data. For the different functional responsibilities of a role, we work with customers to design and optimize the Agent’s behavioral patterns. We also continuously accumulate and annotate large volumes of SOP—standard operating procedure—data for vertical roles, ensuring that the Agent is deeply adapted to real business logic.
Q5: How does your company balance the rapid pace of AI innovation with practical commercial implementation, and what strategic trade-offs does it make?
Zhai Xingji: The core principle guiding that balance is Results as a Service, or RaaS.
In principle: We convert AI capabilities into digital employees for specific roles and charge according to actual business results, such as incremental revenue or cost savings. This ‘base salary plus commission’ model closely aligns our interests with those of customers and motivates us to innovate in ways that improve business outcomes.
In strategy: We focus on specific fields and roles, such as pre-sales, and turn them into standardized role-capability models that can be rapidly replicated across different customers, enabling implementation at scale.
In our trade-offs: We categorically reject two types of projects. The first is the ‘KPI-driven project,’ originating from management pressure rather than a real need. The second is a large project beyond our own capacity to absorb, because excessive customization would divert resources from iteration of the core product. We prioritize small projects that allow us to iterate rapidly and achieve standardization.
Q6: How does YUHE.AI apply Agentic AI technology, and how will it reshape the AI application ecosystem?
Zhai Xingji: We adopted Agentic AI as a core strategy very early and packaged it into out-of-the-box ‘digital employees.’ For example, our digital pre-sales quotation employee for manufacturing uses a Multi-Agent collaboration model that integrates the capabilities of 5 core roles, including a technical assistant and requirements clarification. It can complete tender-document analysis and quotation work that once took 3 days within 20 minutes, while raising accuracy from an industry average of 70% to more than 90%.
We believe Agentic AI will reshape the AI ecosystem in four ways:
Interaction revolution: Moving from ‘instruction-driven’ to ‘goal-driven’ interaction. Users need only state a goal, and an Agent can independently break it down, plan, and deliver the result.
Ecosystem restructuring: Enterprise software will shift from ‘stacks of functional modules’ to ‘networks of collaborating agents,’ with the Agent becoming the ‘central nervous system’ that connects isolated systems such as CRM and ERP systems.
Advancement of value: Moving from ‘reducing costs and improving efficiency’ to ‘creating incremental value.’ AI will shift from a ‘cost center’ to a ‘profit center,’ for example by proactively identifying sales leads.
Shift in competition: The competitive focus will move from a ‘model race’ to ‘barriers in scenarios and data.’ Whoever has the deeper industry understanding and greater accumulation of data will take the lead.
Q7: What opportunities and challenges does YUHE.AI face as it expands AI products globally, and how do its market strategies differ?
Zhai Xingji: The opportunity lies in the high cost of labor overseas, which creates an urgent need to reduce costs and improve efficiency. At the same time, our digital employees, trained on China’s wealth of real-world scenarios, have an ‘asymmetric’ advantage in dealing with complex situations.
The main challenges are differences in technical standards, stringent data-security and compliance requirements, intense competition in global markets, and cultural differences.
Our market strategy is tailored to local conditions:
Japan and South Korea: Focus on localizing the product to fit local industry standards and expand channels through partnerships with local enterprises.
Southeast Asia: Focus on core industries such as manufacturing and logistics, offer cost-effective solutions, and build localized service teams.
Europe and North America: Focus on strengthening interoperability with local SaaS companies and technology platforms, adapting to their standardized software ecosystems, and building the brand.
Q8: When taking AI technology overseas, how does your company address cross-border issues such as data privacy and ethical standards?
Zhai Xingji: Our practical experience focuses on technical architecture and embedding rules:
Adapting the technical architecture: We use a ‘local storage plus domestic training’ architecture. Raw data is stored on server nodes in the customer’s country, while only encrypted and desensitized ‘feature data’—not the raw data—is used for model iteration. This meets local regulatory requirements while preserving the model’s ability to evolve.
Ethical standards—embedded rules and traceable processes: To prevent Agent decisions from causing ‘algorithmic bias’ or ‘unclear responsibility,’ we embed industry standards and enterprise compliance requirements—such as quotation red lines and confidentiality agreements—as foundational rules in the Agent decision flow. We also ensure that every step in an Agent’s decision, including which knowledge it invoked and which rules it used to generate a solution, is traceable and auditable.
Q9: The theme of this conference is ‘Pioneering Intelligence | The Era of the Individual.’ How do you interpret it, and how is AI redefining the commercial value of individuals?
Zhai Xingji: This theme gets to the heart of AI’s disruptive reshaping of ‘individual value.’ It frees individuals from their former dependence on organizational resources and professional skills, transforming them from ‘passive executors’ in a business chain into ‘active centers of value’ that can independently call on intelligent tools and create complete value.
AI’s impact is comprehensive:
In creativity: AI is moving from an ‘assistive tool’ to a ‘co-creation partner,’ helping individuals break through technical barriers and fixed ways of thinking.
In productivity: AI amplifies individual efficiency. With AI, one person can deliver the productivity that previously required a small team, with 24/7 availability.
In commercial value: AI dramatically lowers the resource threshold for individuals to monetize their abilities. Individuals are no longer ‘one link in an organization’s value chain’; they grow into ‘micro-enterprises’ capable of directly delivering complete commercial value.
Q10: Could you share a specific example of your company’s product empowering a team to ‘deliver major results with a small team’?
Zhai Xingji: The ‘digital quotation employee’ we built for a large centrally administered state-owned shipping enterprise is a representative example.
Pain point: In the past, the enterprise’s pre-sales team needed an average of 3 days to handle a complex tender and quotation proposal. The process was cumbersome and depended heavily on the experience of senior experts.
Empowerment: After our Agent started work, it compressed the entire process—including tender analysis, solution generation, and quotation calculation—from 3 days to within 2 hours.
Result: Response time improved to within 1 hour, allowing the team to double the number of projects it could take on. At the same time, because proposals were more precise and responses more timely, the customer repeat-purchase rate rose from 30% to 65%, while average order value increased by 20%–30%. This freed the team to focus on more essential creative work.
Q11: For an ‘AI creator’ in your field, what are the greatest opportunity and most severe challenge today?
Zhai Xingji: The greatest opportunities are:
Technological progress and lower costs: Advances in large-model technology are making AI increasingly capable and increasingly affordable.
Continued growth in market demand: Enterprises urgently need to reduce costs, improve efficiency, and achieve business breakthroughs.
The most severe challenge is:
The demands placed on product and technology iteration are extremely high. Within a limited window of time, we must rapidly iterate scenarios, win mindshare in the market, and establish product leadership.
Q12: Looking ahead over the next 1-3 years, how should ‘individuals’ or ‘small teams’ prepare in terms of skills, thinking, and resources to capture the benefits of the AI era?
Zhai Xingji: The core opportunity for individuals and small teams is to counter the ‘scale advantage’ of giants with ‘asset-light operations, deep specialization, and strong adaptability.’
In skills—T-shaped skills: People must cultivate ‘high-barrier vertical-domain expertise,’ the vertical stroke of the T, as a cognitive moat, while also developing the compound capability to ‘direct AI in completing high-value tasks,’ the horizontal stroke of the T.
In thinking—human-machine collaboration: People must move beyond a mindset of “replacing AI” and toward human-machine collaboration. They should clearly define the areas where people provide core value—creativity, strategy, and emotional connection—hand 80% of routine work to AI, and use 20% of their effort to create “80” of the core value.
In thinking—leveraging the ecosystem: Embrace open source and the API economy, ‘innovating from the shoulders of giants,’ and concentrate limited resources on ‘understanding industry needs’ and ‘packaging differentiated solutions.’
In resources—building networks: The key to resources is not ‘how much you own,’ but ‘how much you connect.’ Three resource networks must be built in advance: a network of industry experts to uncover real pain points, a network of data partners to obtain high-quality industry data, and a lightweight network of ecosystem partners to work with large models, SaaS platforms, and others.