LighthouseIQ

Lighthouse's proprietary AI technology solves a unique data deduplication challenge while migrating over 25 terabytes for an extensive healthcare system. Key Results In 5 months, Lighthouse migrated four databases—with 25 TBs of data—all while keeping the databases active for review and production for current matters. Leveraging our AI technology, Lighthouse created an innovative solution for a large volume of Lotus Notes files originally processed as HTML files by a legacy processing tool. This solution ensured that any new Lotus Notes files would deduplicate against the migrated data, regardless of the file type or the tool used for processing. A Challenging Data Deduplication Problem A large healthcare system had been hosting its data (over 25 TBs of data across four databases) on another vendor’s platform for nearly a decade. The company knew it was time to modernize its eDiscovery program with Lighthouse. In order to do so, all 25 TBs would need to be migrated over to Lighthouse for hosting and future processing. However, in addition to data migration, the company also had a unique deduplication challenge due to the previous vendor’s original processing tool. The company’s data had originally been processed with the vendor’s legacy processing tool—which processed Lotus Notes data as HTML files, rather than the more modern EML version. The prior processing of these files into an HTML format meant that whenever duplicate Lotus Notes files were added to the database and processed using a more modern processing tool, those EML files would not deduplicate against the older HTML files in the databases. With over half their data consisting of Lotus Note files processed by the older tool in HTML format, the company was concerned that this issue would significantly increase review cost and slow down review time. Thus, in addition to the overall migration process, the company came to Lighthouse with an unfortunate Catch-22: in order to modernize its processing and eDiscovery capabilities, it was losing the ability to deduplicate a majority of its data with each new ingestion. Lighthouse Migration Expertise Because of the volume of new clients moving to Lighthouse for eDiscovery support, Lighthouse has developed an entire practice group dedicated to data migration. This group is adept at creating customized solutions to the unique challenges that often arise when migrating data out of legacy systems. The team works closely with each client to understand the scope, types of data, challenges, and future needs so that the data migration process is seamless and efficient. The Lighthouse migration team quickly got to work gathering information from the healthcare company to start this process, paying particular attention to the Lotus Notes deduplication issue. Once all relevant information was gathered, Lighthouse worked with stakeholders from the organization to form a comprehensive migration plan that minimized workflow disruption and included a detailed schedule and workflow for future data. In the process, Lighthouse also developed a custom solution for the Lotus Notes issue using our proprietary AI technology. An Innovative Solution: Lighthouse AI Lighthouse’s advanced AI technology can create a unique hash value for all data, no matter how it was originally processed. The Lighthouse migration team leveraged this innovative technology to create a unique hash value for the Lotus Notes files that were originally processed as HTML files. That hash value could then be matched against any new Lotus Notes files that were added to the database by the company, even when those files were processed as EML files. With this proprietary workflow, the healthcare company was able to seamlessly move to Lighthouse’s eDiscovery platform, which was better equipped to serve its eDiscovery needs—without losing the ability to deduplicate its data. Set Up for Success In just five months, Lighthouse completed a seamless migration of the healthcare company’s data by creating a custom migration plan that minimized blackouts and kept all databases up and running. Importantly, Lighthouse also leveraged its proprietary AI to create an innovative solution to a complex problem, ensuring continued deduplication capability and reduced discovery costs. ‍ Corporate Case Studycase-study; ai; ai-and-analytics; ai-big-data; corporate; corporation; ediscovery; ediscovery-migration; prism; processing; project-management; healthcareediscovery-review; ai-and-analytics; client-successCase-Study, client-success, AI, ai-and-analytics, AI-Big-Data, Corporate, Corporation, eDiscovery, eDiscovery-Migration, Prism, Processing, Project-Management, Healthcare, ediscovery-review, ai-and-analytics

EDRM 2.0 reflects a fundamental shift in discovery: The intelligence that makes document review faster, more focused, and more valuable now begins long before document review itself.For more than two decades, the Electronic Discovery Reference Model has provided a shared framework for understanding how information moves through discovery. Its latest evolution reflects how AI and modern data are shifting where and how that work gets done.EDRM 2.0 introduces a stronger "left lean" toward early assessment and targeted data scoping. It groups Identification, Preservation, Collection, and Processing within a unified Data Acquisition framework and positions Analysis as a continuous activity across the discovery lifecycle.© 2026 EDRM (EDRM.net) Original modified by Lighthouse.These changes reflect how purpose-fit AI is enabling legal teams to pull intelligence out of data earlier, rather than waiting until data is loaded into a document review platform.Better Intelligence Before ReviewAI platforms integrated with purpose-fit AI applications can now help legal teams examine data earlier and with greater precision. Before review even begins, legal teams can now use purpose-fit AI tools and workflows to:Answer targeted questions across even the largest datasets Identify and narrow the likely responsive population before loading it into a review platformIdentify and analyze images and other complex content Identify and classify potentially privileged, confidential, highly sensitive, or issue-relevant content These early insights help counsel make more informed, faster decisions about discovery scope, litigation strategy, and the right path for different types of information.Sending Better Data into ReviewEDRM 2.0 continues to position Review as the nexus where legal relevance and strategic decision-making converge. However, its increased emphasis on upstream analysis shows the growing opportunity to make review more focused and valuable.When legal teams can understand and categorize datasets earlier, they can send a smaller, better-understood, and more strategically relevant population into the review environment. This reduces unnecessary hosting and review costs while allowing attorneys to focus on the nuances and decisions that require more legal judgment. It can also significantly reduce the volume of documents that require attorney review, while making review of the remaining documents faster, more consistent, and more focused.Connecting Intelligence Across the LifecycleThe evolution of the EDRM reflects where discovery is heading. The future of review will be shaped both by what happens inside the review environment and by how effectively legal teams develop intelligence before review and carry it forward through the matter.Realizing this opportunity takes more than applying one AI tool to every problem. Different tasks call for different technologies and workflows, along with the expertise to select the right approach and validate the results.Learn how LighthouseIQ applies purpose-fit, legal-grade AI across the matter lifecycle, supported by experienced professionals and defensible validation, to reduce unnecessary review, accelerate fact development, and strengthen case strategy.

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