Global Pharmaceutical Company cuts cross-matter review costs with Lighthouse AI analytics across 5 concurrent matters

A global pharmaceutical company deployed Machine Learning & Predictive Analytics for E-Discovery & Document Review in Litigation & Disputes. As reported by www.lighthouseglobal.com: 105,300 overlapping documents identified (5 matters).

Maintained by Peter Korpak, Lead EditorHow evidence is checked
105,300Overlapping Documents Identified (5 Matters)
26,450Privilege Codings Reused Across Matters
4,865Redactions Reused Across Matters

Source-reported figures — cited source: www.lighthouseglobal.com

What the global pharmaceutical company was trying to fix

A global pharmaceutical company faced high legal spending and review inefficiency managing multiple concurrent litigation matters. Document review was slow and costly, with no mechanism to leverage attorney work product or data insights across matters. Redundant review of overlapping documents was driving up costs and introducing inconsistency in privilege and responsiveness determinations.

What the global pharmaceutical company deployed

Lighthouse deployed AI-powered analytics to create a custom review workflow for the company's matters. Beyond optimizing individual matter review, Lighthouse connected all five active matters so its AI could identify overlapping documents at the outset of each new matter. Past attorney coding decisions—privilege determinations, redactions, responsiveness classifications—were reused to reduce eyes-on review while improving consistency and accuracy across cases.

Results

Across five connected matters, Lighthouse AI identified over 105,300 overlapping documents, enabling significant processing cost savings and early strategic insights for counsel. The system reused 26,450 privilege codings and 4,865 redactions, eliminating redundant attorney review. The pharmaceutical company's ROI continues to grow as each new matter ingested further refines classification models and expands the pool of reusable work product.

Key Takeaways

  • Cross-matter AI compounds in value: connecting matters unlocks efficiency gains that are impossible on a single matter, as classification models become more accurate with each new dataset ingested.
  • Reusing attorney work product (privilege coding, redactions) across matters reduces both cost and inconsistency risk simultaneously.
  • Early overlap detection at matter intake—before review begins—enables more strategic, data-backed decisions by counsel from day one.

Evidence for the global pharmaceutical company's E-Discovery & Document Review deployment

Reported outcome metrics
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