Turn a research question into an influence network you can argue with — then find out what your study can actually detect, what your data can actually measure, and what got left out along the way.
Free to try · Read the 5-minute guide
The decisions that determine what a study can discover are made before any analysis begins — and they are usually made implicitly.
No statistical method fixes a study that asked the wrong question or missed a confounder. Design errors are permanent — they bound what the study can find.
Real problems span disciplines. No individual commands the breadth to name every relevant mechanism across entomology, hydrology, remote sensing and epidemiology at once.
Months of literature review and scoping collapse into a methods paragraph. What was considered and rejected — the roads not taken — is almost never written down.
A research design is a series of narrowings from the world to what you can measure. ResearchArchitect makes each narrowing explicit instead of silent.
You supply the research question, your data, and the scope of the study. The system proposes the influence network; you correct it. Every variable, every link, and every judgement about what falls inside or outside the study is something you can point at, disagree with, and change.
What you end up with is a design that knows its own boundaries: which relationships matter most, which are observable within your window, and which were deliberately set aside.
How it works, step by step →These are not screenshots. Each one loads into the app itself, with the same tools and the same symbology — refine it, re-run an analysis, or fork it into a study of your own.
ResearchArchitect comes out of work on GeoAI, causal inference and autonomous geographic information systems, and is developed alongside active field studies in vector-borne disease.
Open a sample study to see the shape of it, or begin with your own research question and work outward.