一、A Recurring Challenge

My professional journey has taken me across multiple industries and markets.

I began my career in Shanghai, focusing on big data and digital transformation solutions, where I worked with major institutions such as the Shenzhen Stock Exchange, China Merchants Bank, and Ping An. I later joined Tencent, followed by Shopee, one of Southeast Asia’s leading e-commerce platforms. Eventually, I started my own business and worked with large enterprises, including state-owned companies such as Poly Group.

Across different industries — from finance and real estate to consumer businesses — and through my experience working with organizations of all sizes, I discovered a recurring challenge:

Even the largest enterprises often struggle with disconnected internal systems and fragmented data.

Business departments build their own platforms, data remains isolated across systems, and different teams often operate with inconsistent definitions and metrics.

After becoming an entrepreneur, I experienced this challenge from the perspective of a technology partner.

When many companies approached us, they had already paid the price for previous digital transformation attempts. Some had worked with low-cost outsourcing teams and received solutions that barely functioned. Months later, when they needed changes or new features, no one was willing to touch the code because the original team had already disappeared.

Others had implemented systems across different business processes, only to create more disconnected data islands. Some followed the AI trend and launched impressive AI transformation demos, but when applied to real operations, even basic business data — such as inventory and orders — could not be properly connected.

An industry veteran who was my former manager and had visited companies across many countries once shared his perspective:

China’s digital transformation journey is only about 20% complete.

I agree with this assessment. Recent industry data reveals an even more challenging reality:

• 89.6% of large-scale industrial enterprises in China have already started digital transformation initiatives.
• However, according to the Enterprise Digital Transformation Index Report (2025), based on an analysis of more than 100,000 enterprises, only 15% have entered a substantial transformation stage.
• Among them, only 1.69% have achieved true end-to-end integration across core business operations — the level where digital transformation is genuinely delivering results.

89.6% are investing.
15% are investing in the right direction.Only 1.69% are achieving measurable outcomes.

In my view, the gap comes down to four words:

Technology over data.

True Data + AI transformation cannot be achieved by simply purchasing more software systems. It requires a clear evolution path and a strategic foundation.

This belief became the foundation of GeekOnUp’s approach. We developed what we call the GeekOnUp Three-Layer Model:

Every company’s digital transformation should be viewed through three layers — and more importantly, these layers must be built in the right sequence.

二、GeekOnUp Three-Layer Model

The GeekOnUp Three-Layer Model, proposed by GeekOnUp founder Deng Yichuan, is an enterprise digital transformation framework built around a clear evolution path:

Application Layer → Data Layer → AI Layer

Digital transformation must be developed layer by layer, in the right sequence.

The Application Layer generates and accumulates business data.
The Data Layer connects fragmented systems, standardizes data definitions, and establishes a unified data foundation.
The AI Layer leverages this structured data foundation to enable intelligent decision-making and automation.

Skipping layers and investing in advanced technologies too early is one of the fundamental reasons why many digital transformation initiatives fail.

Application Layer

Mobile apps, mini programs, web platforms, and enterprise management systems — these are where most companies begin their digital transformation journey, and they remain the layer where GeekOnUp serves the largest number of clients.

Although the Application Layer may appear to be the most fundamental part, it determines the quality and potential of everything that follows.

Business data is generated here. User behaviors are captured here. Every future capability built on top of data and AI depends on the foundation created at this stage.

If a company’s applications are fragmented, disconnected, or built with inconsistent data structures across different platforms, it will struggle to move toward deeper digital transformation.

That is why our standards for the Application Layer are highly specific:

• Data structures and definitions must remain consistent across platforms.
• Users should experience the same historical records and information whether they access the system through a mini program, web platform, or mobile app.
• Core user experiences and interactions must be prioritized.
• The architecture must be designed with future data integration and scalability in mind.

The Application Layer is not simply a software delivery project. It is the starting point where an enterprise’s future data assets are created.

Data Layer

A company must first establish effective applications before it can build a data platform. This sequence cannot be bypassed.

The Data Layer is where I have accumulated the deepest expertise, and it is also the layer where I believe the greatest long-term value is created:

Connecting fragmented data across different systems, establishing consistent data standards, building a unified data platform, and transforming data into a strategic asset for business decision-making.

To be transparent, only a limited number of our clients have reached this stage. One of the companies we have worked with most deeply is Newnorth Group, an Australian fresh food supply chain enterprise.

Our partnership began with application systems. Over three years, we have continued to evolve together — from building operational applications to developing a comprehensive data platform, as well as delivering integrated software and hardware solutions covering intelligent sorting, logistics, and warehouse operations.

During this period, the company’s valuation increased multiple times and it is now preparing for its next stage of growth.

This experience reinforced my belief:

The value of the Data Layer can only be realized through long-term partnership.

It is not a one-time project that can simply be tendered, delivered, accepted, and completed.

AI Layer

I believe that over the next three to five years, every enterprise will recognize one critical reality:

AI + BI will become an essential combination for business leaders, and both depend on a strong data foundation.

AI + BI is an inevitable direction of enterprise transformation.

Today, almost every company wants to pursue “AI transformation.” However, one principle I have believed in since founding GeekOnUp is this:

If a company has not yet established digital foundations, lacks standardized data structures, and has no unified understanding of its business data, it cannot successfully achieve AI transformation.

AI is not magic.

Its potential is ultimately determined by the quality of the data behind it.

Trying to implement AI before establishing the Application Layer and Data Layer is like discussing how many floors to build without first laying the foundation.

That is why we chose to validate this approach internally before helping our clients do the same.

Today, GeekOnUp operates without a traditional sales team — not because we cannot hire one, but because we do not need one.

We built our own AI-powered CRM system, allowing one person to handle the workload of three to five sales representatives. From lead synchronization and customer scoring to opportunity assessment, the majority of the process is automated and supported by AI.

We became our own first customer.

By proving that the path from Application Layer → Data Layer → AI Layer works in our own operations, we are able to guide our clients through the same transformation journey with greater confidence.

三、Strategic-Driven Business Operations

The principles above define one thing clearly: GeekOnUp cannot be a traditional “project-based software outsourcing company.”

To help enterprises complete the full journey across the three layers, our business model must align with the long-term nature of digital transformation.

That is why we have made several decisions that may seem unconventional from a traditional industry perspective.

We Work on a Limited Number of Projects at a Time

A technology company with nearly 50 employees and a development team accounting for 90% of the organization is currently working on only a handful of projects.

Once our engineers are assigned to a project, they remain fully dedicated throughout the delivery cycle, without being split across multiple projects.

Why?

Because we believe the value of software is not measured by the number of projects completed, but by whether the solutions actually deliver meaningful business outcomes.

If GeekOnUp operated like traditional software vendors — chasing more projects, accepting every opportunity, outsourcing layers of development, and generating profits through intermediary margins — delivery quality would inevitably decline, project control would disappear, and our reason for existing would be lost.

We Do Not Take Projects Below Our Standards, Regardless of Short-Term Profit

Many traditional software companies compete through aggressive pricing and lower standards. In many cases, this happens because they need to fill idle capacity and keep teams occupied.

We choose a different path.

We would rather have our engineers invest time in internal products and technological innovation than compromise our standards just to win a deal.

Because once survival pressure starts influencing pricing decisions, delivery quality will eventually suffer.

And only high-quality delivery creates the foundation for long-term partnerships.

This is a principle we refuse to compromise:

Short-term gains should never come at the expense of long-term value.

Transparent Pricing Based on Real Value

Our pricing model is straightforward:

Market-based engineering costs + reasonable profit margin.

We do not profit from information asymmetry or unclear pricing structures.

Our value comes from using the same level of investment to deliver more predictable, reliable, and valuable outcomes.

Building Strategic Partnerships, Not Vendor Relationships

The type of partnership we pursue is not a traditional supplier-client relationship.

It is a long-term strategic partnership.

Our collaboration with Newnorth Group in Australia represents this approach.

Today, our relationship has evolved beyond individual projects. The client works with us through a monthly partnership model. When business needs arise, they provide the direction, and our product managers, engineers, and QA teams independently organize priorities and execution.

The client does not need to manage our daily tasks or define every KPI.

A disposable outsourcing relationship creates unnecessary costs for both sides.

The future of software development is not about delivering isolated projects.

It is about becoming a long-term technology partner that grows together with the business.

四、Final Thoughts

If You Are Facing These Challenges, This Is Why We Exist

If you are struggling with the challenges mentioned above — being disappointed by outsourcing partners, investing in multiple systems that cannot communicate with each other, or wanting to adopt AI but not knowing where to begin — this is exactly why we founded GeekOnUp.

If You Are Building at the Application Layer

We help you build the right foundation.

What you receive is not simply an APP or software system that can go live. You receive a digital foundation built with clean, consistent data structures and future-ready architecture from day one.

If You Are Moving Toward the Data Layer

We help you connect fragmented systems, establish unified data standards, and build a true data platform that supports business decision-making.

This is the core capability we brought from our big data background — transforming scattered information into a strategic business asset.

If You Are Exploring AI Transformation

We start with an honest assessment:

Is your data foundation actually ready for AI?

If it is, we help you implement AI capabilities that create real business impact — not just impressive demonstrations.

If it is not, we tell you which layer needs to be strengthened first, rather than selling you an unnecessary “AI transformation” project.

Beyond Technology: Helping You Make Better Decisions

Throughout the entire journey, one thing remains constant:

We help you make the right decisions — including the decision not to spend money when it is unnecessary.

Which technology stack is sufficient today?
Which capabilities do you not need to invest in yet?
Which emerging platforms are worth exploring, and which are not?

These judgments are not separate services we charge for.

They represent what we believe a true consulting partnership should be.

Even if you ultimately choose not to work with us, understanding where your business stands within the Three-Layer Model can already help you avoid unnecessary investments and save significant resources.

Building the right foundation at the Application Layer, while creating a clear path toward the Data Layer and AI Layer — this is how the GeekOnUp Three-Layer Model is applied to every project we deliver.

Our brand philosophy is simple:

Promise less. Deliver more.