Celeus
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Watch a run from upload to package.

A 3-minute walkthrough on a synthetic dataset. Captions are on by default, and the full transcript is under the video.

A synthetic dataset is uploaded, its identifier columns are replaced with pseudonymous codes, a method is suggested and run in R, and the reproducible package is downloaded.
Transcript

Celeus: from a dataset to a reproducible result.

We drop in a synthetic dataset: forty rows, five columns.

Celeus scans every column first. Two of them, patient name and MRN, look like direct identifiers.

The review recommends replacing both columns with coded stand-ins. We apply the recommendation as proposed.

That creates a new, de-identified version. The original upload is kept, and a fresh scan finds no identifiers.

Next, Celeus ranks suitable analyses from the de-identified profile, without calling an AI model. To compare the outcome between the two arms, it recommends Welch's t-test.

The test, outcome and group come pre-filled. We add a research question, which is saved with the results.

The analysis runs in R, the statistical programming language. The wait is shortened in this video.

The result: the treatment group's mean is 6.7 points higher, with a 95 percent confidence interval of 4.25 to 9.15, and p below 0.001.

Below it is a plain-language summary written without AI, and a warning: with forty rows, only large effects can be detected reliably.

An AI interpretation is added on top, clearly labelled. The AI works from summaries and aggregate results, never your data rows, and the computed numbers remain the ground truth.

Here is the exact R code that reproduces this result, with its seed and the engine version.

We check the results and confirm them right here, next to the download. The confirmation is saved with the run, and the reproducible package unlocks: the data, the script and the pinned package versions, together.

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