1. Treeify's Vision
1.1 Why We Built Treeify
Software is being built faster than ever before.
AI coding, agents, MCP, and all kinds of automated workflows are rapidly changing how software gets produced. More and more work is shifting from "people doing the work directly" to "people defining the workflow, while AI executes it." But in test design, many critical capabilities still live inside individual experts' experience. They have not truly been structured, and they have not been distilled into capabilities that AI can understand and execute.
That is exactly why we built Treeify.
What we increasingly see is that the real value is not just in having AI generate some test outputs. The deeper value lies in extracting the judgment, decomposition methods, coverage strategies, and design experience that experts have developed through years of practice, and turning them into Skills that AI can call, execute, and reuse. Only when these capabilities are expressed can AI become more than a tool that answers questions. It can become something that genuinely participates in complex work.
In our view, Skills are the language of AI.
Without Skills, much expert knowledge remains tacit and continues to depend on repeated human involvement. But once those capabilities are organized, expressed, and structured, they can enter AI workflows, be called repeatedly, combined, optimized, and reused, and become productivity that can scale in a sustainable way.
Treeify starts with test design, but we are not trying to build only a test design tool. Through Treeify, we want to explore a broader path: helping experts gradually turn tacit knowledge into explicit knowledge, so it becomes a new kind of asset in the AI era, one that can be accumulated, scaled, and reused.
1.2 What We Want to Change
The first thing we want to change is how expert knowledge exists.
In the past, the most valuable professional capabilities were often held by a small number of experienced people. They lived in personal judgment, work habits, intuition, and project retrospectives. That knowledge was valuable, but because it was not structured, it was hard to standardize, hard to transfer efficiently, and hard to integrate into AI-driven workflow automation.
We want to change that.
We believe that in the AI era, tacit knowledge cannot fully scale in value unless it can be expressed, structured, and consumed by AI. Once that knowledge is distilled into clear Skills, it no longer remains only something an expert knows how to do. It becomes a capability that AI can execute in the same way.
That means the most valuable thing experts do is no longer just completing one task after another themselves. It is preserving their core methods as reusable, composable capability units that can continue creating value over time.
We also want to change how people think about the value of AI.
AI's value should not be limited to "faster output" or "saving some time." Its deeper value lies in whether it can actually take on the knowledge accumulated by human experts and bring that knowledge into large-scale execution. For individuals, this means professional experience has a chance to shift from a service into an asset. For teams, it means experience no longer has to depend on a particular person, but can become a capability the organization can keep using. For the industry, it means a new model of collaboration can emerge: experts provide Skills, AI consumes Skills, and workflows invoke Skills.
That is the change Treeify hopes to drive.
1.3 Treeify's Approach to Test Design
Treeify believes that test design is not just a task for generating output. It is a process of expressing knowledge and preserving capability.
On the surface, test design looks like analyzing requirements, breaking down test objects, and building scenarios. At a deeper level, it is really about continually turning an expert's understanding of business risk, system behavior, coverage logic, and validation strategy into executable methods. In essence, it is the process of making tacit knowledge explicit, step by step.
That is why we believe good test design should not rely only on experts doing the work themselves, nor on a general-purpose AI generating everything in one shot. It should be a process in which expert experience is continuously distilled into Skills, and AI continuously invokes those Skills.
In that process, the expert's most important value is not merely providing answers, but defining methods. AI's most important value is not merely producing content, but executing according to those methods. Only then can test design evolve from work that depends heavily on individual experience into a capability system that can be reused at scale, continuously improved, and eventually automated.
That is also the direction Treeify consistently follows in product design: not only helping users complete test design for the current project, but also helping them extract valuable professional knowledge during each design cycle and preserve it as Skills that AI can continue consuming in the future.
From this perspective, Treeify's long-term vision goes beyond test design itself.
We hope to build a platform where experts create Skills and AI consumes them. On that platform, experts would not only preserve and reuse their own capabilities, but also share, combine, and even trade them as assets. Companies and individuals, in turn, could find the professional Skills that truly fit their own scenarios, so AI workflows are built on real expertise rather than generic generation.
We believe this will become a new way of distributing value in the AI era:
Experts contribute knowledge, Treeify structures capability, AI consumes Skills, and professional experience becomes an asset that can keep creating value.