There is data you can buy. There is data you can scrape. And there is data that is generated only when a professional works within your system for years: we call it decision data (proprietary data). That third category is truly unique, because it cannot be replicated.
What is decision data?
Decision data is the knowledge accumulated over years of using your existing software solution: which case was approved, which exception was made, which customer history led to which recommendation, which notification was ignored, and which one was escalated.
It is the decisions themselves—with their context, timing, and consequences. An accountant who approves financial statements after a 10-year client relationship weighs hundreds of variables. If that decision is embedded in the system—along with all the signals that preceded it—then that is the most useful data an agent can have to support that accountant.
In a previous blog post, I described three levels of domain knowledge. Decision data is the third layer—practical knowledge—but in a documented form: structured, measurable, and usable by agents.
Valuable Agents in 3 Steps
Decision data captures the relationship between input, context, and decision. That relationship is what agents learn from and what makes them increasingly smarter for a specific user. And that mechanism takes years to build. How it works:
Step 1: Customers use the product for their daily work in regulated industries. Every action is recorded. The combination of high-volume routine tasks and low-volume exceptions provides the agent with context.
Step 2: That data is fed to the agents on the platform as structured context for each user. Which situations lead to which decisions? In what ways do professionals deviate from the standard, and why? Those patterns are what set an agent apart.
Step 3: Better agents deliver better results for the customer. Better results mean more usage. More usage means more user knowledge. And that, in turn, makes the agent even better.
Why this cannot be copied
A competitor can use the same model. Build the same infrastructure. They can even replicate the same features. What they cannot copy is 10 years’ worth of a user’s decision-making data—including the context, the exceptions, and the consequences.
Public data that was once unique can now be retrieved by anyone in seconds with a simple search. What matters is the information that becomes available only when you actually do the work in a specific field, for specific clients, and within specific regulatory frameworks. That kind of knowledge is built up only through years of use by professionals who know what they’re doing.
Data architecture has thus become the core of your product strategy. Every workflow you design is also a data design.
Data as the Foundation for Tomorrow’s Workplace
This is why we build WorQX and Qore/AI the way we do. Every workflow on the platform records decisions along with their context. Every agent running on the platform learns and improves based on the work that professionals do there every day.
Jeroen van Eijk previously described what the workplace will look like in 2027 for professionals in regulated sectors: an environment where the work is already prepared before you log in. Decision data makes that environment possible.
Frequently Asked Questions
Decision data (proprietary data) is the recorded decision history of professionals who work on a platform every day: which cases were approved, which exceptions were made, and which signals led to which actions. Unlike purchased or public data, decision data is generated solely through years of use. This makes it the most distinctive source for domain-specific AI agents.
Generic models quickly become comparable and are available to any provider. What sets an agent apart is the context in which it operates: the patterns derived from thousands of real-world decisions in a specific field. These patterns are embedded in decision data and cannot be purchased or replicated by a competitor.
The files and personal data remain the property of the client, who is the data controller; the platform is the data processor. Agents learn from the patterns that emerge from years of use within the platform, in accordance with the terms of the data processing agreement and within the framework of the GDPR and sector-specific laws and regulations. Transparency regarding what an agent learns and the basis for that learning is a strict requirement, especially in regulated sectors where professionals remain responsible for their decisions.
By treating data architecture as the core of your product strategy. Every workflow is also a data design: document decisions along with their context, timing, and consequences, and structure them in a way that allows agents to learn from them. This requires years of consistent use by professionals, and that is precisely why the result is so difficult to replicate.
