Unnamed Am Law 100 Firm

When a high-stakes litigation matter demanded rapid turnaround on hot document reporting, an AmLaw 100 firm ran into a familiar but costly bottleneck: the process of identifying, reading, and summarizing key documents was entirely manual. Reviewers had to sift through large document sets to surface the most critical materials, then hand-craft summaries suitable for partner-level and client stakeholder reporting. Under litigation pressure, this created a compounding problem — the work was time-intensive, the output quality varied by reviewer, and the timeline for delivering actionable insights kept slipping. With stakeholders expecting consistent, structured briefings on a compressed schedule, the manual hot document workflow was simply not built to scale.

The firm worked with Consilio to deploy Aurora AI Investigate and Aurora AI Summarize for Relativity Server within a secure, privately hosted environment — a non-negotiable requirement given the sensitivity of the matter. Aurora AI Investigate automated the identification phase, using large language models to surface the most legally significant documents from the broader review population without requiring reviewers to manually comb through every file. Once key documents were flagged, Aurora AI Summarize generated structured, consistent summaries ready for stakeholder consumption. Running both tools in an integrated workflow meant the investigative output fed directly into the summarization pipeline, eliminating handoff delays and keeping the entire process within the firm's controlled infrastructure.

The integrated AI workflow cut hot document summary preparation time by 60 percent, saving the team 50 to 60 hours on the matter. Beyond raw time savings, the quality and consistency of stakeholder deliverables improved — summaries followed a uniform structure regardless of which reviewer had originally worked the documents, reducing the editorial pass typically needed before materials reached partners or clients. The privately hosted deployment meant the firm could move quickly without compromising data confidentiality, and reviewers reported meaningfully less pressure during the crunch phase of the matter. The result was a workflow that was simultaneously faster, more reliable, and better suited to the security requirements of high-stakes litigation.

Pairing investigative AI with automated summarization creates compounding efficiency gains that neither tool achieves in isolation — identifying hot documents is only half the problem; converting those documents into stakeholder-ready summaries is where time had historically been lost. Private hosting is not optional for AmLaw-caliber matters: the ability to deploy AI within a controlled, client-isolated environment was a prerequisite, not a preference. Consistency is a measurable outcome alongside speed — AI-generated summaries eliminated the variability that comes with multiple reviewers producing materials under deadline pressure, which has downstream value for client reporting quality and partner review time. And the 50–60 hour saving on a single matter reframes the ROI question: the cost of deploying integrated AI tooling should be evaluated against matter-level time savings, not just per-document processing rates.