Case Studies

The most complex matters test outside counsel on every front with overwhelming data volumes, relentless deadlines, and multi-million-dollar outcomes on the line. Lighthouse is purpose-built for these moments. By uniting innovative AI with award-winning expertise, we give outside counsel the speed, precision, and confidence required for the most demanding matters, from high-stakes bankruptcies to sprawling multidistrict litigation and everything in between. So, when a massive second request hit with unforgiving timelines and zero margin for error, our team was ready. Here’s how we delivered speed, precision, and $20M in savings when it mattered most. Proven Performance Under Pressure The Challenge 30+ TB of data 2-month deadline A parallel private antitrust litigation The Lighthouse Advantage: AI + Expert Operations Lighthouse leveraged our proven approach for complex, high stakes matters: advanced AI integrated with skilled operations, review, and subject knowledge expertise: AI at the Core: $11.9M saved on review and production costs Lighthouse’s predictive large language models (LLMs) narrowed the responsive set by over 2M documents and helped reduce privilege review by 45%. Our generative AI then accurately drafted more than 100K privilege log entries, and identified and normalized 15K names and titles. $4.8M saved on key document identification Our experts used AI modeling to quickly surface documents that posed an antitrust risk, surfacing the 270 most important documents out of 4.4M documents in just three weeks. This work eliminated the need for traditional key document searches and issue tagging. No Rework, No Waste $3.5M saved on cross-matter work To support the parallel antitrust litigation, Lighthouse built a secure repository for cross-matter work product reuse and used AI to repurpose work across 680K documents. This work drove consistency and minimized duplicative review between the two matters. Operational Execution: 2 months Scaled and managed a 300-person review team Processed 30TB of data Produced 10TB+ and 20M+ images—error free Implemented a custom workflow to link M365 cloud attachments Why It Matters Second requests are a stress test, but they’re not the only time attorneys face massive data and unforgiving deadlines with millions of dollars at stake. When the pressure is highest, Lighthouse delivers. With AI, expertise, and operational discipline, we give outside counsel what they need to handle every complex matter.

Challenge Antitrust regulators issued a broad, high-stakes HSR Second Request to investigate a global company’s high-profile acquisition. To comply, the company and their outside counsel were faced with analyzing 30+ TB of data in less than a month.Solution  Lighthouse developed an AI driven approach powered by Lighthouse’s proprietary Large Language Models (LLMs) to eliminate relevance review, substantially reduce privilege review, perform privilege logging and assemble the names list, and identify key documents to mitigate risk without linear review.Simultaneously, Lighthouse operational teams executed multiple custom workflows to process and produce over 10TB+ of data and 20M+ images in 3 weeks—flawlessly.Their work included building a custom linking workflow for M365 cloud attachments and a secure data repository for work product reuse in a related antitrust litigation, while ramping a 300-person linear review team via Lighthouse’s Managed Review solution to meet the aggressive production deadline.Lighthouse AI Savings & ROI Breakdown Overall, using Lighthouse AI saved $20M+ with the following workflows: TAR powered by Lighthouses AI proprietary LLMsPrivilege identification powered by Lighthouse AI proprietary LLMsPrivilege log and names list generation via Lighthouse AI proprietary LLMsKey document identification powered by linguistic modeling and AIJunk file analysisCross matter analyticsHighlights of each of these workflows and the associated ROI are below.  Lighthouse’s Proprietary Predictive AI for Relevance: 40% Narrower Responsive Set Than Other TAR Tools Counsel only needed to review around 4,000 documents to stabilize and validate a TAR model backed by Lighthouse’s predictive LLMs that measured 85.91% precision at 76.49% recall. Lighthouse’s predictive AI model for relevance is shown to deliver a 30-40% smaller and more accurate R-set based upon comparative bake-offs. Had Lighthouse implemented a Second Request workflow using commercially available TAR tools, we would have likely seen a much broader responsive set, thereby requiring additional privilege review and production.Specifically, if Lighthouse had used traditional TAR tools, it would have resulted in an estimated additional 2M documents in scope for production, translating into approximately $5.8M in additional privilege review costs and $400,000 in added production expenses—costs avoided by using Lighthouse Responsive AI.Separately, Lighthouse’s Review Management team performed Junk File analysis and identified that around 95,000 documents out of the more than 860,000 non-TAR eligible documents were highly likely to be junk and could undergo a sampling workflow instead of a full linear review.Privilege Review: 45%+ Reduction  Lighthouse built an AI model for privilege that both removed documents from privilege review and accelerated the remainder. Using a tiered approach, counsel determined that around 780,000 documents (out of the more than 1.7M eligible for privilege review) could be removed from privilege review without linear review because they fell below the cutoff score and were unlikely to be privileged.Once the privileged documents were identified, Lighthouse’s generative AI was used to create first-pass privilege descriptions for the more than 100,000 documents on the privilege log. This removed the need for human drafting of log lines. As a result, the log required an investment of mere hours as opposed to days and the heavy expensive of a full contract attorney review and outside counsel QC.Lighthouse also used generative AI to build the names list for the privilege log. Lighthouse’s AI model quickly analyzed around 130,000 documents to identify and provide close to 15,000 normalized names with titles for the privilege log. Key Document Identification The Lighthouse AI team used modeling to quickly surface documents that could pose an antitrust risk to the company. This process eliminated the need for the more dated approach of search+linear review for key documents and issues tags. Out of an initial tranche of 4.4M documents, the Lighthouse team identified roughly 270 documents of the greatest interest and sensitivity and delivered them to counsel in 5 deliveries over the course of just 3 weeks.To provide additional support to counsel, the Lighthouse AI team also categorized the final Responsive document set based on risk profile—classifying a broad set of documents as Likely Risky and Likely Safe over the course of just 1.5 weeks.Work Product Reuse for Cross-Matter EfficiencyThe Second Request had a large set of overlapping data with a concurrent antitrust litigation. To ensure there was no duplicative review and to drive consistency, Lighthouse built a secure data repository that enabled work product reuse between this Second Request and that concurrent litigation and used Lighthouse AI to drive cross matter analytics. With Lighthouse AI, Lighthouse repurposed calls for around 680,000 documents, resulting in a savings of $3.5M between first pass review, 1L QC, and outside counsel QC.Speed, Quality, and Operational Delivery  The scale and complexity of this Second Request required an extraordinary cross-functional effort across several Lighthouse Client Services and Operational teams who worked tirelessly to deliver a seamless, high-quality result under immense time constraints. The core of this matter was completed in under 60 days, demonstrating an exceptional level of execution.Key highlights of the operational delivery include: High-Volume Processing and Custom Workflows: Lighthouse processed over 30TBs of data, ensuring rapid ingestion, indexing, and AI-powered classification. A custom workflow was implemented to link M365 cloud attachments, meeting regulatory requirements.Flawless Data Production at Scale: Within 3 weeks, the Lighthouse team produced over 10TBs of data and more than 20M images, achieving a 100% error-free production result. This ensured compliance without delay or rework.Scalable Review Team for Complex Work Streams: Leveraging our Managed Review capabilities, Lighthouse rapidly scaled a linear review team to 300 professionals. The team expertly navigated multiple complex work streams, dynamically segmenting data to mitigate risk while accelerating the review process to meet the production deadline. The bulk of review ramped and concluded in a mere 4 weeks. This work showcases the coordination, detail, operational excellence and sheer dedication of the Lighthouse team in delivering a timely, high-quality outcome in an intense regulatory investigation.

The ChallengeA major media company received a Letter of Inquiry (LOI) from the FCC, triggering a high-stakes regulatory investigation. The company was required to produce relevant communications within a month—but two weeks in, the legal team still needed to collect over 2 million documents from 16 custodians. Complicating matters further, the company’s software platform was mid-transition, raising serious concerns about data integrity and reporting reliability. The SolutionRecognizing the urgency and complexity of the matter, the media company and its outside counsel turned to Lighthouse. With an immense volume of documents, looming regulatory deadlines, and a technology transition in progress, they needed more than linear review—they needed a strategic partner with forensic, eDiscovery project management, and AI expertise. Within just four days, Lighthouse’s forensics experts collaborated with the company to collect and process all relevant custodian data, including associated family files. From there, our project management team worked with the company and its counsel to apply targeted filtering—focusing on communications between key senders and recipients. This reduced the original 2 million documents to a refined universe of 94,000. Using advanced email threading and junk file analysis, the team further reduced the review set to 59,000 documents. Given the aggressive timeline, volume of documents, and the dataset’s low privilege risk, Lighthouse consultants recommended deploying Relativity aiR for Review. Working closely with inhouse and outside counsel, Lighthouse developed a defensible AI review prompt using an iterative sampling workflow designed to meet stringent recall standards and maximize precision. Only 300 documents were reviewed during this iterative phase. Relativity aiR identified a predicted responsive universe of 28,000 documents. A first-level review was completed in just five days, followed by a quality control review conducted by outside counsel. Final validation confirmed 88% recall and 96% precision—exceeding regulatory and eDiscovery defensibility standards. The ResultsUltimately, 18,000 documents were successfully produced on time, along with an expert declaration on the defensibility of the process from a Lighthouse Strategic Consultant. When the FCC issued a supplemental request, the teams were able to use the aiR-powered workflow once again to quickly review 2,000 additional documents—resulting in the production of the 300 relevant files.

In a high-stakes matter, a Fortune 500 CPG company and its Am Law 200 counsel faced a common eDiscovery challenge with uncommon complexity: a constantly changing review population.The ChallengeAt the heart of this large-scale matter was a significant complication: scope volatility. The Technology-Assisted Review (TAR) universe had to be redefined and recalibrated—multiple times. For most providers, this would derail consistency, timing, and cost.The Solution Lighthouse leaned into the complexity. Using a modular AI framework, we built workflows that could flex in real time; adapting to evolving populations without losing precision. Key solutions included:TAR re-scoping at scale of a 4.6M document universe without destabilizing the model Automated tracking and updates to full-family inclusions and exclusion logic Integrated PII, privilege, and image AI analysis to ensure comprehensive review coverageThe Result Despite the volatility, Lighthouse delivered enterprise-grade performance: 76% precision at 75% recall and the required documents were produced on time. What could have been a fractured and inefficient review was a streamlined effort due to Lighthouse’s ability to manage dynamic review workflows using AI. In today’s litigation and investigation landscape, moving targets are the norm. Lighthouse delivers the consistency, agility, and accuracy needed to keep pace and produce with confidence.

Client: Global academic medical system Stakeholders: CISO, Information Governance, Legal Tech stack: Microsoft 365 + Microsoft Purview (SharePoint, OneDrive, Exchange, Teams) Objective: Identify, label, and protect high‑value IP across M365 Business Challenge Conventional pattern matching missed nuanced research content; labels were inconsistent. Emerging IP taxonomy lacked consistent, enforceable labels across repositories. Conventional pattern matching couldn’t reliably detect unstructured, nuanced IP. Teams needed clarity on when to use Sensitive Info Types (SITs), Exact Data Match (EDM), and Trainable Classifiers, and how to govern them. Wanted to compare outcomes with prior third‑party classifiers. What Lighthouse Did IP Taxonomy + Purview Labels Mapped proprietary IP categories to a label set Blended Classifier Strategy Combined SIT, EDM, and Trainable Classifiers Operationalize Piloted and tuned models aligned with retention/legal hold/eDiscovery, with auto‑labeling and user prompts. Controls Developed change‑management materials Outcomes Common IP Language: Agreed taxonomy mapped to enforceable labels. High‑Confidence Detection: Trainable classifiers surfaced custom IP Consistent Protection: High‑value content auto‑labeled with policy‑driven controls in M365. Governed Workflows: Clear guidance on SIT vs EDM vs Trainable; fewer false positives/negatives; faster to eDiscovery. Timeline Weeks 0–1 — Kickoff + Plan Weeks 2–4 — Design + Setup Weeks 5–9 — Run Pilots Weeks 10–11 — High Level Design + Training Why Microsoft Purview for Data Protection Enterprise-wide strategy - Unified data security, governance, compliance Integrated governance - DLP, retention, legal hold, eDiscovery Flexible detection models - Sensitive Info Types, Exact Data Match, Trainable Classifiers Persistent, label-based protection - Embedded permissions travel with data

When a Fortune 500 CPG company faced a second request tied to a business divestiture, its Am Law 200 counsel partnered with Lighthouse to manage the heavy data lift of 4.6M docs. With tight deadlines, shifting custodians, and complex privilege concerns, the stakes were high—and consistency was critical.The ChallengeVolatile TAR populations and changing custodian scopes Compressed timelines typical of second requestsHigh data volume across diverse file types including video and image The Solution Lighthouse deployed a suite of AI-powered tools to keep the matter on track: AI-driven TAR and privilege/PII detection to streamline review Generative AI + transcription tech enabled fast image/video analysis Modular workflows allowed rapid pivots as scope evolved The Result The AI-led approach let attorneys focus on strategy while Lighthouse handled operational complexity—proving the power of collaborative, AI-enabled review. The matter was completed on time and to the satisfaction of all parties involved. The legal team met their regulatory deadlines, and the CPG client was well-positioned for the next stages of its divestiture. Why It Matters This engagement shows how scalable AI, smart workflows, and strong partnerships can turn regulatory chaos into consistent, repeatable success.

Company Overview A global Fortune 500 manufacturing company with facilities and offices across North America, Latin America, Europe, Asia, and Australia.ChallengesThe company was evaluating Microsoft Purview E5 licenses to support its information protection, insider risk management, data loss prevention, and sensitivity labeling goals. The data protection team needed to validate the efficacy of the Purview tools when applied to company data within their M365 environment. The data project leader shared a list of use cases to test against the platform’s risk identification and alert capabilities. SolutionLighthouse’s consultants designed and ran a four-month pilot to test Microsoft Purview’s sensitivity labeling, Data Loss Prevention (DLP), Insider Risk Management (IRM), and Defender for Cloud Apps capabilities across the client’s live Microsoft 365 environment. The team configured and validated more than 10 distinct data protection policies, including global personal information labels for Exchange, Teams, OneDrive, and endpoint devices. The project included six sensitive information types (such as PII, PCI data, and passport numbers), and piloted risk-based alerts for questionable user and departing employee behavior. Lighthouse developed a detailed Report Card and Recommendations Report, delineating a clear path for full implementation of validated controls company-wide. Key OutcomesDuring the initial four-month engagement, Lighthouse’s experts successfully validated each use case, demonstrating that Microsoft Purview E5 was indeed the correct tool for the client’s data protection needs. The client purchased Purview E5 licenses and engaged Lighthouse to guide the full implementation of all the capabilities we had piloted. By following the guidance of Lighthouse data experts, the company’s data protection team developed an ongoing, flexible data protection strategy and program which mitigated multiple risks by automating data classification, labeling, and user notifications. Lighthouse helped this client accelerate its data protection maturity model and establish best practices, workflows, and classifiers to strengthen data privacy and security. We can do the same for you. Contact an expert to get started.