Watch how DRR-AI helps researchers check manuscripts for disclosure risk — and try it yourself with a sample paper.
Four short videos covering the motivation, the privacy model, and a live walkthrough of the tool.
Why restricted-use data research faces a disclosure bottleneck, and what DRR-AI does about it.
How the ephemeral, zero-retention architecture keeps your manuscript data off our servers.
A step-by-step walkthrough: upload your manuscript, select a framework, and read your audit report.
A real manuscript run through the tool, showing flagged findings and recommendations.
Download one of these sample papers and run it through the Audit Manuscript tool. Each paper contains intentional disclosure risk issues — see if DRR-AI catches them all.
Open Audit ToolContains unrounded sample sizes and a missing SOURCE note. Also includes a true zero cell — see if DRR-AI correctly distinguishes it from a near-zero requiring "<10".
Contains zero cells that may be true zeros or near-zeros requiring "<10" — flagged as Review for author verification before official DRR submission.
These papers are fictional and created solely for demonstration purposes. Any resemblance to real research is coincidental.