Unnamed Am Law 50 Firm

In high-stakes litigation, the speed of evidence identification directly shapes legal strategy and negotiating position. When a national AmLaw 50 law firm faced a time-sensitive matter involving a 138,000-document production, traditional linear review workflows would have consumed more than 30 hours of attorney time before counsel could assess the evidentiary landscape. At standard billing rates for senior litigators, that volume represents significant cost exposure — but more critically, it represents a strategic delay that adversaries can exploit. The firm needed a way to surface high-value documents rapidly without sacrificing the accuracy and defensibility that high-stakes matters demand.

The firm engaged Consilio, a global legal services provider, to deploy advanced AI workflows anchored by the Aurora Legal AI Suite — Consilio's purpose-built platform combining large language models and generative AI capabilities for e-discovery and legal operations. Rather than replacing attorney judgment, the workflow was structured as a human-AI collaboration: Aurora's AI triage layer analyzed and prioritized the 138,000-document corpus, surfacing the most relevant materials for attorney review first. This approach integrated directly into the firm's existing review process, allowing attorneys to concentrate their time on high-value documents identified by the AI rather than processing the full production sequentially. Attorney oversight was maintained throughout to ensure accuracy and defensibility.

The AI-assisted workflow reduced total review time from over 30 hours to just 6 hours — a 5x acceleration across a 138,000-document production.

• Document review time: 30+ hours → 6 hours
• Evidence identification speed: 5x faster
• Production scope: 138,000 documents

Beyond the time savings, the firm achieved faster strategic insight into the evidentiary record, enabling counsel to make earlier and better-informed decisions about case positioning. Lower overall review cost followed directly from the reduced attorney hours. The human-AI collaboration model preserved accuracy and defensibility while compressing the timeline that traditionally constrains litigation response.

• AI triage is most effective in e-discovery when it prioritizes rather than replaces attorney review — surfacing high-value documents first while keeping human judgment in the loop.
• A 5x speed improvement on large document productions is achievable without sacrificing accuracy when AI workflows are paired with experienced legal oversight.
• Time pressure in litigation is a strategic liability; faster evidence identification translates directly into stronger early case assessment and negotiating leverage.
• Purpose-built legal AI platforms (vs. general-purpose LLMs) are better suited for defensible e-discovery workflows where chain-of-custody and accuracy standards apply.
• Firms should evaluate AI review tools not just on speed but on how well they integrate with existing attorney workflows to minimize adoption friction.