How Advanced Analytics and AI Transformed Early Case Assessment in a Multi-State Wage Dispute
Legal professionals today face unprecedented challenges in employment litigation: massive data volumes, tight deadlines, and complex risk profiles. In a recent employment litigation matter involving a biotechnology company, our team leveraged the combined power of Generative AI—through eDiscovery AI’s Early Case Intelligence (ECI) with Case Elements—and Relativity’s Concept Clustering and Visualization to deliver rapid, actionable insights and streamline the review process.
A biotechnology company was facing potential litigation from several former executives. The plaintiffs’ claims spanned multiple employment-related causes of action in multiple states – some even with multiple employment-related departments within the same state. They each filed claims against both the company and its Board of Directors individually for unpaid wages and PTO as well as minimal damages and attorney’s fees. This all amounted to over $3.5 million.
Financial difficulties for the company began several years ago, leading to salary reductions and repeated furloughs for executives. Internal communications and board minutes revealed that the company’s leadership were aware of the mounting unpaid obligations. Despite attempts to secure loans, personal contributions from Board members, and potential acquisition deals, the wage liabilities persisted.
There were multiple unique aspects to this case which provided challenges that would be hard to meet with traditional methods of review. First was the potential for individual liability among board members under multiple state wage statutes. Board members faced personal risk for unpaid wages, with some providing short-term loans to cover payroll deficits. Next, was the need to assess potential conflicts and privilege given that one of the former executives was the CFO/General Counsel. Finally, the matter was further complicated by an asset purchase agreement with another biotech company, raising questions about the assumption of wage liabilities post-acquisition.
Challenges Faced
Legal professionals handling this matter encountered several significant challenges:
- Volume and Complexity:
- Over 245,000 documents—including emails, contracts, payroll records, board minutes, and financial statements—required review and analysis.
- The data set included conceptually diverse materials, making it difficult to identify relevant clusters and key facts without advanced analytics.
- Time Sensitivity:
- The case was in active mediation, demanding rapid early case assessment and strategic clarity to guide negotiations.
- Tight deadlines required accelerated insight generation from which to draw conclusions on case strategy.
Why Combine Generative AI and Concept Clustering?
Tools like Cluster Visualization, and Nearby Clusters provide intuitive maps of document clusters which helped our case team to quickly drill into subclusters and related concepts. This was also helpful in the development and validation phases of prompting. We were able to identify clusters where likely relevant and non-relevant documents would be found, enabling richness to be controlled for development phases and to verify that random samples had a good mix as well.
Generative AI – through eDiscovery AI’s Early Case Intelligence with Case Elements – identified key dates and individuals, as well as key documents such as payment records, and contractual language, surfacing critical facts and relationships – clarifying connections between plaintiffs, board members, and disputed transactions. It also produced a comprehensive case summary and strategic recommendations, supporting legal analysis and potential mediation strategy.
How the Technologies Worked Together
By integrating Relativity’s Concept Clustering and Visualization with Generative AI-powered Early Case Intelligence, the legal team achieved:
- Accelerated Early Case Assessment: Clustering enabled rapid exploration and organization of unfamiliar data sets, while Generative AI surfaced key facts and risks.
- Strategic Clarity: GenerativeAI-driven analysis provided actionable recommendations. Interactive visualizations helped identify relevant clusters.
Data-Driven Mediation Preparation: The combined approach reduced review time from weeks to days – completing the review in-house and identifying enough information to resolve the matter in about 4 days.
Real Impact and Client Feedback
The Case Team benefited from:
- Clearer understanding of case strengths, risks, and defenses
- Reduced discovery costs and improved outcomes
- Confidence in defensible, data-driven strategy
Our client noted:
“The documents turned out to be very helpful for resolving the matter. I’m looking forward to using these tools again in future cases.”
Conclusion: The Future of Legal Analytics
The combination of Generative AI and Unsupervised Learning – such as Concept Clustering – is transforming legal case intelligence. By leveraging technologies in combination, law firms can move beyond traditional review to strategic, insight-driven litigation support, delivering better results for clients in complex matters.
Contact us to discover how advanced analytics and AI can empower your legal team in employment litigation, wage disputes, and beyond.