How ChinaMarket uses digital tools to make cross-border sourcing faster and more accessible for smaller businesses
A rack of colourful scarves. PHOTO: UNSPLASH
The 5th RCEP (Shandong) Import Commodities Expo opened this week at the Linyi International Expo Center, bringing together more than 5,300 buyers and over 400 exhibitors from 48 countries. Alongside the scale of the event, a quieter shift was visible in how trade itself is being organised.
ChinaMarket, the official platform of Linyi Mall, used the expo to show how sourcing is moving from manual coordination to software-led systems. On the first day, it hosted procurement matchmaking sessions and signed agreements with buyer groups from Argentina, South Korea and Ghana. But the focus was less on the deals themselves and more on the mechanism behind them.
The platform operates as a structured network of verified manufacturers, grouped by industrial clusters. Instead of buyers searching supplier by supplier, the system uses data and AI tools to match demand with production capacity. At the expo, this process was made visible through real-time data screens and guided sourcing sessions, where procurement teams connected directly with factories across categories such as building materials, textiles and electronics.
"Sourcing suppliers separately was time-consuming and inefficient. ChinaMarket accurately matches our needs and recommends reliable factories, saving us considerable effort," commented an Argentine buyer.
The underlying problem being addressed is not new. Cross-border sourcing is often slow, fragmented and dependent on intermediaries. What is changing is how that process is being compressed. By combining supplier verification, demand matching and communication into a single system, platforms like ChinaMarket aim to shorten sourcing cycles. They also reduce uncertainty in procurement decisions.
Financing is another layer where the model is evolving. Even when suppliers and buyers are matched efficiently, access to capital can still slow transactions down. Small and medium-sized firms often face constraints around payment terms and access to credit in international trade.
ChinaMarket’s “data + order financing” model links transaction data with financial services, allowing funding decisions to be tied more directly to verified orders rather than external collateral. In practice, this shifts part of the risk assessment from institutions to platform-level data.
The company is also extending this structure into agricultural supply chains. At the expo, it signed an agreement with a local government in Yinan County to build a digitally managed agricultural belt. The model combines sourcing at origin with platform distribution, with an emphasis on traceability for buyers across RCEP markets. This reflects a broader attempt to standardise supply visibility in sectors that are typically less digitised.
Geographically, the platform has been expanding into Southeast Asia. It has launched a digital marketplace in Malaysia and established operations in Indonesia, including support for government-linked procurement projects. These moves suggest a focus on embedding the platform within regional trade flows rather than operating as a standalone marketplace.
"We aim to be a 'super connector' between Chinese industrial belts and global markets", said Quan Chuanxiao, Chairman of Depth Digital Technology Group and ChinaMarket. "By digitizing the cross-border trade process, we solve trust and efficiency issues, making it simpler, faster, and more reliable for overseas buyers to source from China".
What emerges from the expo is less about a single platform and more about a shift in infrastructure. Trade is gradually moving toward systems where discovery, verification, negotiation and financing are handled within integrated digital layers. The question is not whether sourcing can be digitised, but how reliably these systems can scale across industries where trust and execution still depend on physical outcomes.
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A look at how motivation, not metrics, is becoming the real frontier in fitness tech
Updated
February 7, 2026 2:18 PM
A group of people running together. PHOTO: FREEPIK
Most running apps focus on measurement. Distance, pace, heart rate, badges. They record activity well, but struggle to help users maintain consistency over time. As a result, many people track diligently at first, then gradually disengage.
That drop-off has pushed developers to rethink what fitness technology is actually for. Instead of just documenting activity, some platforms are now trying to influence behaviour itself. Paceful, an AI-powered running platform developed by SportsTech startup xCREW, is part of that shift — not by adding more metrics, but by focusing on how people stay consistent. The platform is built on a simple behavioural insight: most people don’t stop exercising because they don’t care about health. They stop because routines are fragile. Miss a few days and the habit collapses. Technology that focuses only on performance metrics doesn’t solve that. Systems that reinforce consistency, belonging and feedback loops might.
Instead of treating running as a solo, data-driven task, Paceful is built around two ideas: behavioural incentives and social alignment. The system turns real-world running activity into tangible rewards and it uses AI to connect runners to people, clubs and challenges that fit how and where they actually run.
At the technical level, Paceful connects with existing fitness ecosystems. Users can import workout data from platforms like Apple Health and Strava rather than starting from scratch. Once inside the system, AI models analyse pace, frequency, location and participation patterns. That data is used to recommend running partners, clubs and group challenges that match each runner’s habits and context.
What makes this approach different is not the tracking itself, but what the platform does with the data it collects. Running distance and consistency become inputs for a reward system that offers physical-world incentives, such as gear, race entries or gift cards. The idea is to link effort to something concrete, rather than abstract. The company also built the system around community logic rather than individual competition. Even solo runners are placed into challenge formats designed to simulate the motivation of a group. In practice, that means users feel part of a shared structure even when running alone.
During a six-month beta phase in the US, xCREW tested Paceful with more than 4,000 running clubs and around 50,000 runners. According to the company, users increased their running frequency significantly and weekly retention remained unusually high for a fitness platform. One beta tester summed it up this way: “Strava just logs records, but Paceful rewards you for every run, which is a completely different motivation”.
The company has raised seed funding and plans to expand the platform beyond running, walking, trekking, cycling and swimming. Instead of asking how accurately technology can measure the body, platforms like Paceful are asking a different question: how technology might influence everyday behaviour. Not by adding more data, but by shaping the conditions around effort, feedback and social connection.
As AI becomes more common in consumer products, its real impact may depend less on how advanced the models are and more on what they are applied to. In this case, the focus isn’t speed or performance — it’s consistency. And whether systems like this can meaningfully support it over time.