Turning 11M Docs Into a Cross-Matter Intelligence System with LighthouseIQ

The Challenge

A national healthcare provider faced 14 related matters across 9 jurisdictions, with 11M documents dispersed across multiple vendors, databases, and case teams.

Redundant Review Is a Data Problem, Not a Legal One

With a traditional eDiscovery model, each matter would have required reprocessing, rehosting, and/or rereviewing large portions of the same data. Data insights and work product would be siloed inside individual matters and within disparate legal teams. This would severely escalate costs and drive inconsistent outcomes and operational drag.

The Solution

Lighthouse recognized that the problem wasn’t just data volume. It was the absence of a system that could learn across matters and apply that intelligence forward.

With LighthouseIQ, counsel could take a fundamentally different approach—using a centralized, AI-backed data system guided by expert judgment, where decisions, insights, and work product flow seamlessly between matters and legal teams.

AI-Backed Results

• Reduced 11M documents to 90K requiring review
• Reused 100K coding decisions across 14 related matters
• Avoided duplicate hosting, processing, and review of 1.2M documents
• Enabled instant productions from a national database with LighthouseIQ
• $650K in cost savings delivered with consistency and defensibility built in, not traded off

Building Human-Guided AI at Multidistrict Scale

Step 1: An AI-Powered Data Repository, Expertly Designed

Lighthouse migrated all 11M documents (from both Relativity and non-Relativity sources) into a single LighthouseIQ hosting environment. Lighthouse experts designed the repository architecture upfront to support cross-matter reuse and long-term litigation strategy.

Lighthouse eliminated duplicate hosting, processing, and review of 1.2M documents.

Step 2: AI Normalization and Cross-Matter Matching

Within the repository, LighthouseIQ normalized documents and applied proprietary hashing to identify duplicates, near-duplicates, and previously reviewed content across matters. Lighthouse experts validated how matches and inherited decisions were applied, ensuring accuracy, consistency, and defensibility across jurisdictions.

Lighthouse reused 100K coding decisions across matters.

Step 3: AI-Guided Prioritization, Expert Review Strategy

Lighthouse review experts designed one strategic review plan for all 14 matters that lowered costs and maximized data reuse and cross-matter insights. Using cross-matter intelligence, IQ Review identified 150K documents (from within the 11M housed in the repository) that were most likely to be responsive across jurisdictions.

This dataset was published to the national review database and fully reviewed by an experienced Lighthouse review team (trained by Lighthouse review managers) to categorize each document for both national and jurisdictional responsiveness. After review, Lighthouse copied this strategic production set to each jurisdictional database. This approach kept hosting costs drastically lower for each individual matter, while providing all local case teams with an immediate first production, well ahead of production deadlines.

Out of 11M documents, just 90K required human review.

Step 4: Continuous Learning Through a Human-in-the-Loop Feedback Cycle

After production, expert-approved coding decisions were fed back into the repository. LighthouseIQ automatically matched those decisions to corresponding documents across matters, creating immediate efficiencies while preserving expert intent.

With every matter, the system became: more informed, more consistent, more cost-effective.

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The Results: A System That Gets Smarter Over Time

By using LighthouseIQ, a sprawling, multidistrict litigation environment was transformed into a reusable intelligence system. The client achieved significant cost savings and faster productions, without sacrificing judgment, consistency, or defensibility.

In the process, LighthouseIQ delivered $650K in cost savings.