DC HOLDINGS (HKEX: 00861) has announced a new achievement under its "xᴬᴵ·Supply Chain" ecosystem co-creation initiative. Leveraging its proprietary AI First FDE implementation methodology, the company has successfully secured a contract with a leading domestic campus network operator. The agreement involves providing an intelligent operations solution for end-user (C-end) engagement, empowering a traditional business-to-business (B2B) enterprise to enhance its C-end operational capabilities. This initiative effectively combines deep mining of existing data value with business efficiency upgrades to achieve business model innovation. This marks another successful application of the AI First FDE model.

The client, with years of experience operating campus networks across numerous universities nationwide, has amassed vast amounts of existing user data, including consumption patterns and service plan subscriptions. However, its historical operations were heavily focused on the B2B sector, lacking refined operational capabilities for C-end users. Faced with this massive data repository, key challenges for its transition from B2B to B2C operations included efficiently identifying potential churn risks, formulating precise intervention strategies, and quantifying the actual value of operational actions. Under traditional models, the cycle from proposing operational needs to product development and implementation is lengthy and costly, making it difficult to support agile operational innovation.

Upon engagement, DC HOLDINGS' AI First FDE team abandoned the traditional heavy-asset approach of "building a platform first, then finding use cases." Instead, they focused on the client's actual business scenarios from the outset. During the reconnaissance phase, the team completed basic requirement confirmation and built a demonstrable system in just two days. Utilizing real data samples provided by the client, the team rapidly constructed an AI-powered user churn prevention operations platform. This platform covered core functional modules such as operational overviews, user segmentation, strategy configuration, and effectiveness analysis, and came with pre-configured strategy templates and automated tasks. This created a complete closed-loop system from user risk identification to quantified feedback on intervention actions.

This collaboration fully demonstrates the core advantages of the AI First FDE model. First is its lightweight and rapid delivery; the initial proof-of-concept (POC) version required only 5-6 person-days of actual input, significantly reducing trial-and-error costs. Second is its scenario-driven, tangible value; the project started with the client's most urgent need—churn prediction. The system can automatically label and risk-score all users, with full processing taking merely 13.6 minutes, a task that would traditionally require days or even weeks using manual Excel-based segmentation. Third is its quantifiable and iterative nature; through a pre- and post-intervention snapshot mechanism, it automatically quantifies recovery rates and AI-driven incremental gains, making operational outcomes "visible and calculable."

"The client is an industry expert with extensive experience in campus operations, but the shortfall was being immersed in traditional methods and lacking the ability to combine AI technology for broader strategic thinking," the project lead reflected. "Our strengths lie in two areas: first, the foundational capabilities accumulated over years of data governance and scenario transformation; second, the AI First FDE model allows clients to see a demo within days, validate a minimum viable product (MVP) within weeks, and even leverage AI to propose operational strategies beyond the client's conventional thinking. A client might conceive 5 strategies, but we can propose 10, with two or three new ideas representing a breakthrough for them. This was previously impossible during the pre-sales phase."

The success of this project not only validates the applicability of the AI First FDE model across various operational scenarios but also showcases the effectiveness of the standardized pathway under the "xᴬᴵ·Supply Chain" ecosystem initiative. This pathway progresses from a 1-3 day high-value AI diagnostic, to completing scenario MVP validation within two weeks, to the client deciding on subsequent collaboration depth based on actual results. As projects advance, the accumulated data assets and business rules will continuously grow, creating a positive flywheel effect where "more projects lead to richer assets and stronger capabilities."

Moving forward, DC HOLDINGS will continue to leverage its AI First FDE methodology and core technological capabilities to collaborate with more industry partners on scenario co-creation. The aim is to truly integrate AI into real enterprise workflows, progressing from "seeing data" to "aiding decision-making," and assisting various industries in making the leap from digital quantitative change to intelligent qualitative transformation.