Since joining Structify, I’ve spent (read: all of) my time thinking about data—specifically, how to make inherently unstructured information sensical both to humans and to AI agents.
This process is part creativity, part precision. Or as I like to think of it: art meets science.
One of the most powerful aspects of structifying (soon-to-be-official term for using Structify) your data is that you get to decide how it’s shaped. You’re not just working with a fixed format—you’re designing the schema itself: the tables, the properties, and the relationships between them.
It’s not just about capturing data, it’s about how you model it.
We recently tackled a use case involving clinical trial data—specifically, structuring outcome data like graphs, charts, and results across multiple dosage groups. Our first attempt? A single table with all possible outcomes crammed in as properties.
Spoiler: it didn’t work.
After some whiteboarding and rethinking, we split it into a cleaner, more modular schema. The result made more sense to users and to the AI agents processing the data. You can see what we came up with here.
If you’re building a schema, start here:
Designing schemas is a creative act. The structure you create can shape how data is understood, queried, and displayed. That flexibility is powerful—but it also means the choices you make matter.
Of course, creativity without clarity leads to chaos. That’s where precision comes in.
When designing a schema, especially one intended for machine consumption, precision is non-negotiable. You need to define elements clearly and avoid ambiguity that might trip up your model—or your users.