Am Law 100 Firm

Am Law 100 firms routinely face litigation matters requiring review of millions of documents under court-imposed deadlines, where the cost of traditional linear review can reach tens of millions of dollars on a single case. In complex commercial litigation and regulatory investigations, every document must be assessed for relevance and privilege — a process that scales poorly with document volume. The firm needed a defensible, repeatable review methodology that could handle massive productions without proportionally expanding attorney headcount or blowing case budgets.

The firm deployed Everlaw's AI-assisted review platform, leveraging its predictive coding capability — a machine learning model trained on reviewer decisions to rank unreviewed documents by likely relevance. Rather than assigning documents sequentially, the system continuously updated its model as attorneys coded documents, surfacing the most relevant materials first and deprioritizing likely non-responsive content. Built-in quality controls, including recall estimation and validation sampling, gave the team the audit trail needed to defend the review methodology to opposing counsel and courts. The workflow integrated directly into the firm's existing review process, allowing attorneys to apply AI prioritization without changing their coding interface.

The implementation delivered a 50% reduction in document review time compared to conventional linear review on the same matter type. Beyond speed, the AI-assisted approach achieved higher recall rates than manual review — meaning relevant documents surfaced by the predictive model were less likely to be missed than under purely human review conducted under time pressure. Key outcomes included:

• 50% faster review versus linear workflows
• Higher recall than manual review, reducing risk of missing key evidence
• Defensible, auditable review protocol accepted in litigation context
• Attorneys focused coding effort on high-probability relevant documents, improving consistency

• Defensibility requires documentation: predictive coding is court-accepted, but firms must log model iterations, validation sampling, and recall estimates to withstand challenge.
• Model quality depends on reviewer consistency: the predictive model learns from attorney decisions, so early training rounds benefit from senior reviewer involvement to set reliable relevance signals.
• Recall outperforms speed as the primary metric: the goal isn't just finishing faster — it's finding more of what matters; AI review can exceed human recall even when humans have more time.
• Integration with existing workflow reduces adoption friction: deploying within a familiar review platform accelerated attorney uptake without retraining on new tooling.