Last week, we outlined the three steps for strategically leveraging AI in your litigation or investigation: understanding your data, conducting a RAPID Review, and building your case for trial. This week, we will do a deeper dive into your data. The foundation of effective AI-assisted litigation is a well-organized dataset and UnitedLex can help you get there. Using UnitedLex technologists, data scientists, AI-consultants, and AI-educated attorneys, our curated technology stack has the tools you need to set yourself up for success.
In the world of litigation and compliance, eDiscovery has long been a labor-intensive process which has the potential to become more labor-intensive as data volumes exponentially increase. Traditionally, identifying your data set begins with keyword lists and manual custodian scoping. However, this is an approach that’s reactive, rigid, and often misses the forest for the trees. AI changes the game, offering a proactive, intelligent, and adaptive way to explore data before you even get to defining search terms.
Instead of starting with assumptions, today’s AI-driven eDiscovery begins by building a live, data-driven map of your evidence. This foundational step transforms how legal teams understand and interact with their data through the use of:
- Automated Data Mapping: AI tools can now identify custodians, entities, and communication links across platforms like Outlook, Teams, shared drives, and more. This creates a dynamic network of who said what, when, and to whom—without manual input.
- Semantic Clustering: Rather than relying on keywords, AI groups documents by concept. Whether it’s “pricing discussions,” “contract renewals,” or “regulatory reporting,” semantic clustering surfaces meaningful themes that traditional methods might overlook.
- Relevance Prioritization: These clusters are then ranked by their likely importance to the issues in dispute, helping teams focus on what matters most.
- Intelligent Filtering: AI automatically flags duplicates, low-value material, and non-responsive system data, streamlining the review process and reducing noise.
Once the dataset is mapped, AI doesn’t stop—it begins analyzing. This turns Early Case Assessment (ECA) from a static snapshot into a continuous intelligence layer using:
- Automated Insight Extraction: AI detects early communication patterns, spikes in volume, and recurring topics, offering a preview of potential hot spots.
- Entity & Sentiment Analysis: By identifying key players and shifts in emotional tone, AI can flag potential risk points or contentious exchanges.
- Conceptual Trend Surfacing: Dominant themes emerge that keyword searches might miss, giving legal teams a deeper understanding of the case landscape.
- Dynamic Cost Forecasting: AI can simulate different culling or review strategies, helping teams manage budget and scope with precision.
While ECA is in progress identifying your reviewable set, UnitedLex also uses First Look, a proprietary process combining experts, AI models, and analytics to identify key facts within hours minimizing your upfront costs and enabling early decisions based on facts and data. Using your “hot documents” to jumpstart your timeline provides greater strategic alignment and positive downstream impacts on your reviewable set.
As reviewers tag documents for responsiveness or privilege, AI refines its understanding. This feedback loop is the heart of Continuous Active Learning (CAL) and/or GenAI, providing:
- Automated Retraining Loops: Systems can retrain themselves based on reviewer input, improving accuracy without manual intervention.
- Uncertainty Routing: Documents that fall into gray areas are automatically flagged and routed for human review, ensuring quality control.
- End-to-End Oversight: From ingestion to production, AI maintains a consistent standard of review, reducing errors and accelerating timelines.
Within all of these AI capabilities, the rise of agentic AI—autonomous agents that act on behalf of users—adds a new layer. These agents can continuously monitor and refine dataset mapping, adapting to new inputs and evolving case strategies in real time.
Agentic AI can proactively suggest ECA strategies, monitor emerging risks, and even recommend adjustments based on evolving case dynamics—making it a strategic partner, not just a tool.
Conclusion
eDiscovery is no longer just about finding documents – it’s about understanding them. With AI and agentic systems, legal teams can move from reactive searching to proactive insight. The result? Smarter strategy, faster timelines, and better outcomes.
Next week, we’ll be diving into conducting a RAPID Review.
