Shrink your data for Review. Shrink your timeline and costs.

Law In Order has significant experience with uncommon data types which can derail a project and inflate costs, and with Smart DeDuper, we built a tool that allows us to address these scenarios smoothly and without delay or additional costs to you or your client.

Users can tag unique, master and duplicate documents based on an individual item or a family group of items. A fully customised and user-friendly interface function helps users to review the duplicates and QA the inconsistencies. This helps remove messaging duplicates not only on standard email software but also on collaborative systems such as shared whiteboards, interactive chat, meeting and conference software, etc.

Watch our demonstration video (8:43)

FAQ Smart DeDuper

Smart DeDuper is a proprietary tool by Law In Order that identifies and tags unique, master, and duplicate documents. It significantly reduces the volume of data for review, saving time and costs while maintaining accuracy across standard email systems and collaborative platforms like chat and meeting software.

Yes, Smart DeDuper is designed to process and streamline even uncommon data types that often derail projects. This ensures a smooth workflow without unnecessary delays or cost escalations.

Smart DeDuper removes messaging duplicates across email software and collaborative platforms, including shared whiteboards, interactive chat systems, and video conferencing tools, ensuring a clean and accurate data set for review.

Absolutely. Smart DeDuper offers a fully customised and intuitive interface, allowing reviewers to easily tag documents, manage duplicates, and perform quality assurance on inconsistencies.
No, Smart DeDuper is included as part of Law In Order’s services, ensuring cost-effective and efficient data management without extra fees or licenses.
Smart DeDuper’s tagging and QA features help identify inconsistencies in duplicate or family documents, ensuring that the review process is accurate, consistent, and defensible across all data sets.