Eccang uses Qoder to break the boundary between technical and non-technical teams, evolving from "tool users" to "agent managers."
A Top 3 SaaS company serving tens of thousands of cross-border sellers, with core business processes spanning over 20 stages and covering tax laws and exchange rates across multiple countries — yet this year, it did something that surprised its peers: it introduced Qoder, starting with 1,500 seat-months and covering both R&D and non-R&D staff.
This was not an impulse purchase. It was Eccang Technology's most honest response to the idea that "AI is not a choice, but a must-answer question."
To understand why Eccang made such a big commitment, you first need to see how difficult its business really is.
Eccang's two product lines — ERP and WMS — support sellers through the entire process of selling from China to the world. A single shipment from China to the U.S. involves more than 20 key steps. Every step is a potential pitfall: different countries have different tax laws, exchange rates, time zones, and platform policies; Amazon, eBay, and independent stores each operate by their own rules; bosses, operations staff, finance teams, supply chain managers, and overseas warehouse leaders must collaborate in highly complex ways; SKU management, cross-border financial consolidation, and real-time warehouse scheduling are all indispensable.
As Eccang CMO Gong Zhihao put it: "In the past, sellers profited from information gaps. Now they need brand globalization, compliance, and profitability. The system used to be a ledger; now it needs to be a radar and a brain."
Sellers want faster, more accurate, and more manpower-efficient services, all while maintaining stability. These four demands weigh heavily on R&D — the pressure is easy to imagine.
So the question becomes —
Eccang's VP of Technology Mo Mingyi chose not to simply add headcount.
Earlier this year, he restructured the entire product and R&D team by product line and technical engineering, breaking it down into finer-grained collaboration units specifically adapted to the AI Coding team model. What does this mean? It means the entire chain from requirements to delivery has been redesigned around "human-AI collaboration."
In the past, product managers wrote documents, UI designers produced graphics, front-end developers built interfaces, back-end developers built systems, and QA conducted acceptance testing. The chain was long, and every step introduced losses; information decayed continuously as it was handed off.
Now, things are completely different. Requirements already interact with AI at the documentation stage — documents are converted into Markdown formats that AI can more easily understand, and product managers can shift left to quickly produce demos for validation. More importantly, front-end and back-end code can now be written together. AI understands the entire project structure, so communication between front-end and back-end no longer relies solely on an API document as the single interface.
The most obvious change Mo Mingyi observed is this: Mid-level engineers can now independently design complex systems. Tasks that previously only senior architects could handle can now be tackled by mid-level engineers with Qoder. He himself is an example — a new project he had planned to hire two people for was eventually completed by one person using Qoder and went live quickly.
But a new question arises: with so many AI coding tools on the market, why did Eccang settle on Qoder?
Mo Mingyi's answer is straightforward and pragmatic.
First, the toolchain is complete. From document management to the full R&D workflow, Qoder includes expert teams, custom agents, and Skill packaging. These are not nice-to-have features; they are infrastructure that transforms individual capabilities into organizational capabilities. Eccang doesn't want a few programmers using AI to improve their own efficiency — it wants the entire team collaborating with the same tools and the same language.
Second, stability. As an organizational productivity tool, stability is a hard requirement. Qoder has proven reliable in this regard and is suitable for long-term enterprise use. As Mo Mingyi put it clearly: "This is an organizational productivity tool, not an individual behavior."
Third, reverse documentation capability. RepoWiki generates documentation with one click, reverse-engineering the logic of legacy projects into standardized documents. Eccang has been around for 13 years, and its architecture has been upgraded countless times. Documentation is updated every year, but everyone knows how thoroughly it is actually updated. Now AI can reverse-engineer directly from the code, which is equivalent to giving legacy projects a thorough "health check."
Fourth, team management. The backend shows each employee's consumption, usage ratio, language preferences, and team efficiency metrics, and allows Token allocation on demand. Management is no longer a black box.
With the selection made, how did it actually perform?
Mo Mingyi gave several recent examples.
Development manpower savings: A project he had planned to hire two people for was completed by one person using Qoder and is now live. This is not a proof of concept — it is a real product.
Logistics page restructuring: A product manager felt the logistics quote page was too complex and wanted to change it. In the past, this would have required back-and-forth with the tech team, with long communication cycles and uncontrollable outcomes. Now the product manager can handle the page interactions and validate with AI independently, quickly deciding "whether this can be done" — without waiting for the tech team's schedule feedback.
Internal CRM implementation: Completed in ten days and rolled out company-wide the following week. In the past, such internal demands always ranked behind business needs, and buying an external solution cost money. Now Qoder has made it lightweight. The question is no longer whether resources allow it, but whether it is a priority.
Delivery model transformation: From "writing code" to "defining requirements + reviewing output." In Mo Mingyi's own words: "Write it, and it's done." This means developers no longer dwell on syntax details but focus on whether the business logic is correct and whether the architecture design is sound.
But at this point, many companies will ask a more fundamental question —
Cross-border ERP involves finance, inventory, and orders — data security is the bottom line. Deng Zhanzhao, Senior AI Business Manager at Alibaba Cloud, answered this question from three levels.
First, environment isolation. Qoder and QoderWork are both deployed in a sandbox environment on local PCs. Code operations, generation, and execution do not leak outside; data never leaves the machine.
Second, permission control. Fine-grained access control protects core modules, with different roles accessing different levels. For example, when modifying order settlement logic, Qoder first analyzes which other modules depend on this module and provides impact reminders — so programmers can see at a glance what changes will affect.
Third, generation validation. Every piece of generated code undergoes security analysis, vulnerability scanning, and dependency conflict detection, and only enters the repository after multiple validations. It can also automatically generate test cases to support defensive programming.
Finally, human-AI collaborative acceptance. AI-generated results are reviewed and decided by developers; humans move from executors to decision-makers. Qoder elevates the human role one level up.
With security addressed, Qoder's penetration at Eccang goes far beyond the R&D team.
The 1,500 seat-months are not just for programmers. Eccang's marketing, operations, and customer service teams are all using QoderWork.
Gong Zhihao shared his experience. A product PPT page used to take 1–2 days; now it takes 15–30 minutes. A conference-level PPT can be generated in less than half a day. This is not crude work — if your aesthetic sense is good and your descriptions are precise, the output is genuinely usable.
The change in official website construction is even greater. The marketing team used to have to request slots from the tech team, and customer needs always came first, leaving marketing requests at the bottom of the queue. Now Gong Zhihao uses QoderWork himself: he throws the old website in, lets AI analyze the problems, and AI outputs eight diagnostic items. Then he asks AI to redesign it, and the resulting prototypes are taken directly to team meetings. He jokingly told Mo Mingyi, "My credits are running out a bit fast — can you add some?" — because 80% of the pages were already built by AI.
Customer service is also changing. The customer operations team uses Skills to package common scripts, and logistics reconciliation and complaint handling now run through AI workflows without waiting for tech team scheduling. Gong Zhihao also packaged a Skill for sellers that finds 1688 factories from Amazon hot products. When sellers saw the results, they asked, "Was this edited?" — No, that was the level AI produced directly.
Gong Zhihao summed it up in one sentence: "Humanities professionals now have productivity. It used to be the privilege of STEM professionals; now everyone has it."
This "technical democratization" is not only reflected internally, but also in Eccang's customer-facing products.
Mado AI, launched by Eccang this year, does not stack features on top of the old ERP. It is rebuilt from scratch in an AI-native way.
This decision itself is a story. At the time, there were two options: one was to add AI features to the existing system, and the other was to rebuild it in an AI way. Eccang chose the latter. The reason is simple — only AI-native architecture can follow AI's value chain.
The entire project team was newly assembled, and the toolchain, Skill management, scenario configuration, and agent publishing were all completed on the Qoder platform. The delivery model has completely changed — instead of writing large amounts of code and manual configuration, the team now only needs to understand customer needs, produce Skills, configure scenarios, and publish directly. The product manager's role has also changed: instead of drawing interfaces, they now dig up more business scenarios, translate them into technical language, and dispatch them to back-end developers to build Skills before publishing.
The results speak for themselves: Mado AI's weekly and monthly active users doubled, customers are not only active but also paying, and computing consumption exceeded expectations.
Gong Zhihao's metaphor is vivid: "Using Mado AI is like immediately having a 70–80 point operations director." — Especially for cross-border sellers in second- and third-tier cities, where it is hard to hire a good operations director, this is true technical democratization.
The product has changed, and the organization must evolve with it —
Junior engineers use Qoder to fill in business understanding, while mid-level engineers can design complex systems. Cross-language capability becomes the norm — Mo Mingyi requires everyone to cross at least one language this year; Java developers must be able to use Python. "If you can't cross over, you need to give a reason, or it's hard to justify." This requirement would have been almost impossible two years ago. Now, with Qoder, environment errors are handled by AI, framework selection is handled by AI, and code is handled by AI.
Role boundaries are blurring. Product managers can produce demos, QA uses AI to accelerate, and the marketing team builds the official website itself. Everyone is "shifting left"; the chain is no longer one-way transmission, but integrated collaboration. Mo Mingyi made an analogy: if you don't use good tools well, you become the bottleneck in this team — not because you aren't working hard, but because everyone else is accelerating while you are running in place.
Performance is no longer measured by the number of requirements delivered, but by whether "AI-produced features quickly meet customer expectations." Incentives have also changed — hackathons have become business scenario entrepreneurship competitions, internal AI sharing sessions have been held every month for six consecutive months, and efficiency improvement projects only assess requirement delivery logic and AI usage. Value distribution has shifted from labor-intensive to intelligence-intensive.
Mo Mingyi and Gong Zhihao gave practical advice from technical and operational perspectives — lessons from their own pitfalls.
● Top-down initiative: Only with top-level push can AI truly land. It cannot rely solely on individual interest. Eccang's strategy is company-wide certification, one account per person — start using it first.
● Start with small, new projects: Low risk, easy to deliver results, and builds confidence. Don't rush to refactor legacy projects all at once.
● Make knowledge accumulation readable for AI: In the past, documents were written for humans to read; now they are written for AI to read — use the same tool, the same language, and a format that makes it easier for AI to understand.
● Develop a "driving sense": Getting a license doesn't mean you can drive. Use it boldly and test narrow roads to find the boundaries. As Mo Mingyi said: "Maybe you start driving and end up flying, opening up an air route — that's possible too."
● Avoid mismatched expectations: AI is not all-powerful, nor is it something to give up on after two tries. Gong Zhihao has seen two extremes — one thinks AI can do everything for free, and the other gives up after two attempts. The right posture is to stay curious and first run through a small business scenario.
● Standards first: The technical engineering team establishes standards so that latercomers can calibrate their boundaries when interacting with AI through the standards. If you say "it can't handle this" after typing five words, that's not AI's problem.
● Long-termism: This is a sustained battle that requires patience. Be firm in strategy, flexible in tactics.
Eccang's story is not a narrative of "AI replacing humans," but a story of how an organization, with Qoder, evolves from "tool users" to "agent managers."
From AI Coding across the entire R&D team, to the "humanities professional productivity" of non-technical teams, to the intelligent agent factory model of Mado AI — Qoder is not playing the role of a code completion tool, but an engineering foundation that makes organizational knowledge explicit, capabilities reusable, and quality verifiable.
This article is based on the joint live broadcast by Eccang Technology and Alibaba Cloud, "Eccang × Qoder: AI Empowering Global Expansion."
The Cross-Border E-Commerce Business Is Far More Complex Than You Think
To understand why Eccang made such a big commitment, you first need to see how difficult its business really is.
Eccang's two product lines — ERP and WMS — support sellers through the entire process of selling from China to the world. A single shipment from China to the U.S. involves more than 20 key steps. Every step is a potential pitfall: different countries have different tax laws, exchange rates, time zones, and platform policies; Amazon, eBay, and independent stores each operate by their own rules; bosses, operations staff, finance teams, supply chain managers, and overseas warehouse leaders must collaborate in highly complex ways; SKU management, cross-border financial consolidation, and real-time warehouse scheduling are all indispensable.
As Eccang CMO Gong Zhihao put it: "In the past, sellers profited from information gaps. Now they need brand globalization, compliance, and profitability. The system used to be a ledger; now it needs to be a radar and a brain."
Sellers want faster, more accurate, and more manpower-efficient services, all while maintaining stability. These four demands weigh heavily on R&D — the pressure is easy to imagine.
So the question becomes —
How Does a 200-Person R&D Team Handle Massive Demand?
Eccang's VP of Technology Mo Mingyi chose not to simply add headcount.
Earlier this year, he restructured the entire product and R&D team by product line and technical engineering, breaking it down into finer-grained collaboration units specifically adapted to the AI Coding team model. What does this mean? It means the entire chain from requirements to delivery has been redesigned around "human-AI collaboration."
In the past, product managers wrote documents, UI designers produced graphics, front-end developers built interfaces, back-end developers built systems, and QA conducted acceptance testing. The chain was long, and every step introduced losses; information decayed continuously as it was handed off.
Now, things are completely different. Requirements already interact with AI at the documentation stage — documents are converted into Markdown formats that AI can more easily understand, and product managers can shift left to quickly produce demos for validation. More importantly, front-end and back-end code can now be written together. AI understands the entire project structure, so communication between front-end and back-end no longer relies solely on an API document as the single interface.
The most obvious change Mo Mingyi observed is this: Mid-level engineers can now independently design complex systems. Tasks that previously only senior architects could handle can now be tackled by mid-level engineers with Qoder. He himself is an example — a new project he had planned to hire two people for was eventually completed by one person using Qoder and went live quickly.
But a new question arises: with so many AI coding tools on the market, why did Eccang settle on Qoder?
Why Qoder? Four Reasons
Mo Mingyi's answer is straightforward and pragmatic.
First, the toolchain is complete. From document management to the full R&D workflow, Qoder includes expert teams, custom agents, and Skill packaging. These are not nice-to-have features; they are infrastructure that transforms individual capabilities into organizational capabilities. Eccang doesn't want a few programmers using AI to improve their own efficiency — it wants the entire team collaborating with the same tools and the same language.
Second, stability. As an organizational productivity tool, stability is a hard requirement. Qoder has proven reliable in this regard and is suitable for long-term enterprise use. As Mo Mingyi put it clearly: "This is an organizational productivity tool, not an individual behavior."
Third, reverse documentation capability. RepoWiki generates documentation with one click, reverse-engineering the logic of legacy projects into standardized documents. Eccang has been around for 13 years, and its architecture has been upgraded countless times. Documentation is updated every year, but everyone knows how thoroughly it is actually updated. Now AI can reverse-engineer directly from the code, which is equivalent to giving legacy projects a thorough "health check."
Fourth, team management. The backend shows each employee's consumption, usage ratio, language preferences, and team efficiency metrics, and allows Token allocation on demand. Management is no longer a black box.
With the selection made, how did it actually perform?
Four Real-World Cases of Qoder at Eccang
Mo Mingyi gave several recent examples.
Development manpower savings: A project he had planned to hire two people for was completed by one person using Qoder and is now live. This is not a proof of concept — it is a real product.
Logistics page restructuring: A product manager felt the logistics quote page was too complex and wanted to change it. In the past, this would have required back-and-forth with the tech team, with long communication cycles and uncontrollable outcomes. Now the product manager can handle the page interactions and validate with AI independently, quickly deciding "whether this can be done" — without waiting for the tech team's schedule feedback.
Internal CRM implementation: Completed in ten days and rolled out company-wide the following week. In the past, such internal demands always ranked behind business needs, and buying an external solution cost money. Now Qoder has made it lightweight. The question is no longer whether resources allow it, but whether it is a priority.
Delivery model transformation: From "writing code" to "defining requirements + reviewing output." In Mo Mingyi's own words: "Write it, and it's done." This means developers no longer dwell on syntax details but focus on whether the business logic is correct and whether the architecture design is sound.
But at this point, many companies will ask a more fundamental question —
How Does Qoder Ensure Enterprise-Grade Security?
Cross-border ERP involves finance, inventory, and orders — data security is the bottom line. Deng Zhanzhao, Senior AI Business Manager at Alibaba Cloud, answered this question from three levels.
First, environment isolation. Qoder and QoderWork are both deployed in a sandbox environment on local PCs. Code operations, generation, and execution do not leak outside; data never leaves the machine.
Second, permission control. Fine-grained access control protects core modules, with different roles accessing different levels. For example, when modifying order settlement logic, Qoder first analyzes which other modules depend on this module and provides impact reminders — so programmers can see at a glance what changes will affect.
Third, generation validation. Every piece of generated code undergoes security analysis, vulnerability scanning, and dependency conflict detection, and only enters the repository after multiple validations. It can also automatically generate test cases to support defensive programming.
Finally, human-AI collaborative acceptance. AI-generated results are reviewed and decided by developers; humans move from executors to decision-makers. Qoder elevates the human role one level up.
With security addressed, Qoder's penetration at Eccang goes far beyond the R&D team.
Beyond R&D: "Technical Democratization" for Non-Technical Teams
The 1,500 seat-months are not just for programmers. Eccang's marketing, operations, and customer service teams are all using QoderWork.
Gong Zhihao shared his experience. A product PPT page used to take 1–2 days; now it takes 15–30 minutes. A conference-level PPT can be generated in less than half a day. This is not crude work — if your aesthetic sense is good and your descriptions are precise, the output is genuinely usable.
The change in official website construction is even greater. The marketing team used to have to request slots from the tech team, and customer needs always came first, leaving marketing requests at the bottom of the queue. Now Gong Zhihao uses QoderWork himself: he throws the old website in, lets AI analyze the problems, and AI outputs eight diagnostic items. Then he asks AI to redesign it, and the resulting prototypes are taken directly to team meetings. He jokingly told Mo Mingyi, "My credits are running out a bit fast — can you add some?" — because 80% of the pages were already built by AI.
Customer service is also changing. The customer operations team uses Skills to package common scripts, and logistics reconciliation and complaint handling now run through AI workflows without waiting for tech team scheduling. Gong Zhihao also packaged a Skill for sellers that finds 1688 factories from Amazon hot products. When sellers saw the results, they asked, "Was this edited?" — No, that was the level AI produced directly.
Gong Zhihao summed it up in one sentence: "Humanities professionals now have productivity. It used to be the privilege of STEM professionals; now everyone has it."
This "technical democratization" is not only reflected internally, but also in Eccang's customer-facing products.
Mado AI: Building an AI Product the Qoder Way
Mado AI, launched by Eccang this year, does not stack features on top of the old ERP. It is rebuilt from scratch in an AI-native way.
This decision itself is a story. At the time, there were two options: one was to add AI features to the existing system, and the other was to rebuild it in an AI way. Eccang chose the latter. The reason is simple — only AI-native architecture can follow AI's value chain.
The entire project team was newly assembled, and the toolchain, Skill management, scenario configuration, and agent publishing were all completed on the Qoder platform. The delivery model has completely changed — instead of writing large amounts of code and manual configuration, the team now only needs to understand customer needs, produce Skills, configure scenarios, and publish directly. The product manager's role has also changed: instead of drawing interfaces, they now dig up more business scenarios, translate them into technical language, and dispatch them to back-end developers to build Skills before publishing.
The results speak for themselves: Mado AI's weekly and monthly active users doubled, customers are not only active but also paying, and computing consumption exceeded expectations.
Gong Zhihao's metaphor is vivid: "Using Mado AI is like immediately having a 70–80 point operations director." — Especially for cross-border sellers in second- and third-tier cities, where it is hard to hire a good operations director, this is true technical democratization.
The product has changed, and the organization must evolve with it —
How Does the Organization Evolve? Three Paths
Talent Model: Specialist → Generalist + Specialist
Junior engineers use Qoder to fill in business understanding, while mid-level engineers can design complex systems. Cross-language capability becomes the norm — Mo Mingyi requires everyone to cross at least one language this year; Java developers must be able to use Python. "If you can't cross over, you need to give a reason, or it's hard to justify." This requirement would have been almost impossible two years ago. Now, with Qoder, environment errors are handled by AI, framework selection is handled by AI, and code is handled by AI.
Organizational Structure: Pyramid → Networked Collaboration
Role boundaries are blurring. Product managers can produce demos, QA uses AI to accelerate, and the marketing team builds the official website itself. Everyone is "shifting left"; the chain is no longer one-way transmission, but integrated collaboration. Mo Mingyi made an analogy: if you don't use good tools well, you become the bottleneck in this team — not because you aren't working hard, but because everyone else is accelerating while you are running in place.
Value Distribution: Hours-Driven → Value Creation
Performance is no longer measured by the number of requirements delivered, but by whether "AI-produced features quickly meet customer expectations." Incentives have also changed — hackathons have become business scenario entrepreneurship competitions, internal AI sharing sessions have been held every month for six consecutive months, and efficiency improvement projects only assess requirement delivery logic and AI usage. Value distribution has shifted from labor-intensive to intelligence-intensive.
Advice for Those Still Watching
Mo Mingyi and Gong Zhihao gave practical advice from technical and operational perspectives — lessons from their own pitfalls.
● Top-down initiative: Only with top-level push can AI truly land. It cannot rely solely on individual interest. Eccang's strategy is company-wide certification, one account per person — start using it first.● Start with small, new projects: Low risk, easy to deliver results, and builds confidence. Don't rush to refactor legacy projects all at once.
● Make knowledge accumulation readable for AI: In the past, documents were written for humans to read; now they are written for AI to read — use the same tool, the same language, and a format that makes it easier for AI to understand.
● Develop a "driving sense": Getting a license doesn't mean you can drive. Use it boldly and test narrow roads to find the boundaries. As Mo Mingyi said: "Maybe you start driving and end up flying, opening up an air route — that's possible too."
● Avoid mismatched expectations: AI is not all-powerful, nor is it something to give up on after two tries. Gong Zhihao has seen two extremes — one thinks AI can do everything for free, and the other gives up after two attempts. The right posture is to stay curious and first run through a small business scenario.
● Standards first: The technical engineering team establishes standards so that latercomers can calibrate their boundaries when interacting with AI through the standards. If you say "it can't handle this" after typing five words, that's not AI's problem.
● Long-termism: This is a sustained battle that requires patience. Be firm in strategy, flexible in tactics.
Closing Words
Eccang's story is not a narrative of "AI replacing humans," but a story of how an organization, with Qoder, evolves from "tool users" to "agent managers."
From AI Coding across the entire R&D team, to the "humanities professional productivity" of non-technical teams, to the intelligent agent factory model of Mado AI — Qoder is not playing the role of a code completion tool, but an engineering foundation that makes organizational knowledge explicit, capabilities reusable, and quality verifiable.
After using Qoder, people who have been writing code for more than ten years find it easier and easier. — Mo Mingyi
This article is based on the joint live broadcast by Eccang Technology and Alibaba Cloud, "Eccang × Qoder: AI Empowering Global Expansion."