Unnamed Am Law 100 Firm

For AmLaw 100 firms handling high-stakes litigation, regulatory investigations, or complex arbitrations, hot document reporting is a critical deliverable — key documents flagged during review must be summarized quickly and accurately for supervising attorneys and clients. At the scale these matters typically involve, with reviewers working across large document populations under tight deadlines, manual summarization became a significant bottleneck. Each hot document required individual attention to draft, review, and format summaries, consuming hours of senior reviewer time per matter and introducing inconsistency in how findings were communicated to stakeholders. The cumulative cost was 50 to 60 hours of preparation time that could not be recaptured.

The firm deployed Consilio's Aurora AI suite — specifically Aurora AI Investigate and Aurora AI Summarize — integrated directly within Relativity Server, the firm's existing document review platform. This meant reviewers could trigger AI-driven investigation and summarization without leaving their established workflow. Critically, the deployment used a privately hosted environment, ensuring that privileged and confidential matter data never left the firm's controlled infrastructure — a non-negotiable requirement for large law firms subject to strict client confidentiality obligations. Aurora AI Investigate used large language models to surface relevant connections and context across the document population, while Aurora AI Summarize automated the drafting of structured summaries, removing the manual writing step from the hot document workflow entirely.

Summary preparation time dropped by 60%, saving the review team 50 to 60 hours across the matter — time that could be redirected to higher-value legal analysis. Beyond the headline efficiency gain, the workflow change delivered measurable qualitative improvements:

• Reviewers reported reduced workload pressure during peak review periods
• Stakeholders received document insights faster and with greater consistency across the matter
• Automated summarization eliminated reviewer-to-reviewer variability in how hot documents were described and reported

The integration within Relativity Server meant adoption required no significant change management — the AI layer fit directly into the team's existing review process.

• Embedding AI tools within an existing platform (Relativity) removes adoption friction and accelerates time-to-value compared to standalone solutions.
• A privately hosted deployment model is often a prerequisite for GenAI adoption in large law firms — security architecture should be evaluated before tool selection, not after.
• Pairing investigative AI with automated summarization creates compounding gains: better document context leads to more accurate summaries, not just faster ones.
• Automating structured outputs like hot document reports is a high-ROI entry point for legal AI — the task is repetitive, high-volume, and tolerance for inconsistency is low.