<p>DEEPEXI TECH (01384.HK) -4.200 (-6.061%)，作为首个企业级大型模型人工智能应用股票和企业级 Token 生产力平台公司，其创始人、执行董事、董事长兼首席执行官赵杰辉今天（14 日）在大陆媒体上发表了一篇关于人工智能行业洞察的文章。他指出，企业在人工智能实施过程中面临的困难的核心解决方案之一在于 “本体大型模型”。这一技术不仅弥补了通用模型在专有业务逻辑上的不足，还将之前高成本的 “手动定制” 人工智能项目转变为标准化产品，直接推动商业模式向 “订阅制” 转变。这为人工智能企业从 “概念演示” 走向 “创造实际成果” 提供了明确的路径。</p>
<p>赵杰辉指出，市场上可用的通用模型在语义理解方面表现出色，但缺乏企业的真实内部业务逻辑。以制造业为例，两家不同的汽车制造商可能会遇到相同的机器报警，但其根本原因可能完全不同。这种专有的故障排除经验和规则在公共在线数据或标准操作手册中并不存在，使得通用模型在处理深入的企业运营时常常无能为力。企业真正需要的是具备深厚业务规划能力的专职人工智能员工。</p>
<p>为了突破这一瓶颈，DEEPEXI TECH 推出了专门为企业量身定制的 Deepexi 本体大型模型。该模型基于过去八年为近 400 家大型客户服务所积累的庞大数据集构建。这包括 108 个 “业务本体”，深入覆盖制造业、消费零售、医疗保健、金融和政府事务等关键行业。这些核心运营数据从未出现在公共语料库中，成功建立了一个技术和数据的护城河，使得纯模型开发者难以复制。</p>
<p>在商业化方面，本体大型模型使 DEEPEXI TECH 在工程经济学上实现了飞跃。赵在文章中表示，企业人工智能应用之前依赖前线部署工程师（FDEs）在客户现场进行手动实例化和定制，类似于美国巨头 Palantir 验证的深度工程服务模型。通过本体大型模型，DEEPEXI TECH 成功提炼并 “模型化” 了这些繁重的任务，显著降低了每个客户的定制成本，成为中国市场这一路径的产品化先锋。</p>
<p>Deepexi 企业大型模型采用了 “能力训练行业本体预训练客户本体注入” 的三层训练架构。前两层作为固定成本，在大客户基础上摊销，使得每个客户的定制成本随着规模经济显著下降。这推动了 DEEPEXI TECH 从传统的项目咨询模式转变为 “Token 消费 + 人工智能员工工时订阅” 模式。该商业模式摆脱了对重度定制的依赖，能够随着规模线性扩展，显著缩短盈利周期。</p>
<p>赵总结道，虽然通用模型在成本方面降低了 Token 单位成本，但 Deepexi 本体大型模型提升了 Token 的商业价值密度，真正为企业人工智能应用带来了经济效益。作为一家纯 B2B 企业级大型模型人工智能应用和企业级 Token 生产力平台公司，DEEPEXI TECH 正在利用这一独特定位和订阅模式实现可扩展的货币化，并持续提升运营质量。（ad/da）（香港股票报价延迟至少 15 分钟。）</p>

滴普科技

<p>DEEPEXI TECH 的创始人赵杰辉强调了通用人工智能模型在解决企业特定挑战方面的局限性。他介绍了 “本体大模型” 作为一种解决方案，将高成本的定制化人工智能项目转变为标准化的订阅产品。该模型利用来自近 400 个不同行业客户的专有数据，实现了显著的成本降低，并将项目咨询转变为可扩展的订阅模式。DEEPEXI TECH 旨在提升企业人工智能应用的经济价值，同时提高运营效率</p>

<p>DEEPEXI TECH (01384.HK) -4.200 (-6.061%)  , the first enterprise-level large model AI application stock and an enterprise-level Token productivity platform company, saw its founder, Executive Director, Chairman and Chief Executive Officer Zhao Jiehui publish an article on artificial intelligence industry insights in mainland media today (14th). He pointed out that one of the core solutions to difficulties in enterprise AI implementation lies in the &#34;ontology large model&#34;. This technology not only compensates for the lack of proprietary business logic in general models, but also transforms previously high-cost &#34;manually customized&#34; AI projects into standardized products, directly driving a shift in business models toward a &#34;subscription-based&#34; model. This provides a clear pathway for AI enterprises to move from &#34;concept demonstration&#34; to &#34;creating tangible results.&#34;</p>
<div class="lb-trans"><p>DEEPEXI TECH (01384.HK) -4.200 (-6.061%)，作为首个企业级大型模型人工智能应用股票和企业级 Token 生产力平台公司，其创始人、执行董事、董事长兼首席执行官赵杰辉今天（14 日）在大陆媒体上发表了一篇关于人工智能行业洞察的文章。他指出，企业在人工智能实施过程中面临的困难的核心解决方案之一在于 “本体大型模型”。这一技术不仅弥补了通用模型在专有业务逻辑上的不足，还将之前高成本的 “手动定制” 人工智能项目转变为标准化产品，直接推动商业模式向 “订阅制” 转变。这为人工智能企业从 “概念演示” 走向 “创造实际成果” 提供了明确的路径。</p>
</div><p>Zhao Jiehui noted that general models available in the market excel at semantic understanding but lack real internal business logic of enterprises. Taking the manufacturing industry as an example, two different automakers may encounter the same machine alarm, yet the underlying causes could be entirely different. Such proprietary troubleshooting experience and rules are not available in public online data or standard operation manuals, leaving general models often helpless when dealing with in-depth enterprise operations. What enterprises truly need are dedicated AI employees equipped with deep business planning capabilities.</p>
<div class="lb-trans"><p>赵杰辉指出，市场上可用的通用模型在语义理解方面表现出色，但缺乏企业的真实内部业务逻辑。以制造业为例，两家不同的汽车制造商可能会遇到相同的机器报警，但其根本原因可能完全不同。这种专有的故障排除经验和规则在公共在线数据或标准操作手册中并不存在，使得通用模型在处理深入的企业运营时常常无能为力。企业真正需要的是具备深厚业务规划能力的专职人工智能员工。</p>
</div><p>To break through this bottleneck, DEEPEXI TECH has launched the Deepexi Ontology Large Model tailored specifically for enterprises. The model is built on the vast dataset accumulated over the past eight years from serving nearly 400 large clients. This includes 108 &#34;business ontologies&#34; deeply covering key industries such as manufacturing, consumer retail, healthcare, finance and government affairs. These core operational data have never appeared in public corpora, successfully establishing a technological and data moat that pure model developers find difficult to replicate.</p>
<div class="lb-trans"><p>为了突破这一瓶颈，DEEPEXI TECH 推出了专门为企业量身定制的 Deepexi 本体大型模型。该模型基于过去八年为近 400 家大型客户服务所积累的庞大数据集构建。这包括 108 个 “业务本体”，深入覆盖制造业、消费零售、医疗保健、金融和政府事务等关键行业。这些核心运营数据从未出现在公共语料库中，成功建立了一个技术和数据的护城河，使得纯模型开发者难以复制。</p>
</div><p>In terms of commercialization, the ontology large model has enabled DEEPEXI TECH to achieve a leap in engineering economics. Zhao stated in the article that enterprise AI applications previously relied on frontline deployed engineers (FDEs) to conduct manual instantiation and customization on-site at client locations, similar to the deep engineering service model validated by US giant Palantir. Through the ontology large model, DEEPEXI TECH has successfully distilled and &#34;modelized&#34; these heavy tasks, significantly reducing customization costs per client and becoming a productization pioneer of this pathway in the China market.</p>
<div class="lb-trans"><p>在商业化方面，本体大型模型使 DEEPEXI TECH 在工程经济学上实现了飞跃。赵在文章中表示，企业人工智能应用之前依赖前线部署工程师（FDEs）在客户现场进行手动实例化和定制，类似于美国巨头 Palantir 验证的深度工程服务模型。通过本体大型模型，DEEPEXI TECH 成功提炼并 “模型化” 了这些繁重的任务，显著降低了每个客户的定制成本，成为中国市场这一路径的产品化先锋。</p>
</div><p>The Deepexi enterprise large model adopts a three-layer training architecture of &#34;capability training industry ontology pre-training customer ontology injection.&#34; The first two layers, as fixed costs, are amortized across a large client base, enabling the customization cost per client to decline significantly with economies of scale. This has driven DEEPEXI TECHs transformation from a traditional project-based consulting model to a &#34;Token consumption + AI employee man-hour subscription&#34; model. The business model has shed its reliance on heavy customization and can expand linearly with scale, substantially shortening the profit cycle.</p>
<div class="lb-trans"><p>Deepexi 企业大型模型采用了 “能力训练行业本体预训练客户本体注入” 的三层训练架构。前两层作为固定成本，在大客户基础上摊销，使得每个客户的定制成本随着规模经济显著下降。这推动了 DEEPEXI TECH 从传统的项目咨询模式转变为 “Token 消费 + 人工智能员工工时订阅” 模式。该商业模式摆脱了对重度定制的依赖，能够随着规模线性扩展，显著缩短盈利周期。</p>
</div><p>Zhao concluded that while general models reduce Token unit costs on the cost side, the Deepexi Ontology Large Model enhances the business value density of Tokens, truly delivering economic benefits for enterprise AI applications. As a pure B2B enterprise-level large model AI application and enterprise-level Token productivity platform company, DEEPEXI TECH is leveraging this unique positioning and subscription model to achieve scalable monetization and continuously improve operational quality. (ad/da)(HK stocks quote is delayed for at least 15 mins.)</p>
<div class="lb-trans"><p>赵总结道，虽然通用模型在成本方面降低了 Token 单位成本，但 Deepexi 本体大型模型提升了 Token 的商业价值密度，真正为企业人工智能应用带来了经济效益。作为一家纯 B2B 企业级大型模型人工智能应用和企业级 Token 生产力平台公司，DEEPEXI TECH 正在利用这一独特定位和订阅模式实现可扩展的货币化，并持续提升运营质量。（ad/da）（香港股票报价延迟至少 15 分钟。）</p>
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滴普科技创始人表示，通用 AI 模型在解决企业痛点方面存在困难；‘本体大模型’ 正在推动订阅制的转型