Ropes & Gray has developed a transactional diligence AI in partnership with OpenAI to accelerate the document review that precedes many acquisitions. The tool performs an initial pass through large data rooms, inventories and groups related materials by theme or issue, and produces interim summaries and a final report formatted to the firm’s standards for lawyers to review.
Transactional diligence often forces legal teams to comb through vast volumes of contracts and supporting documents on tight timelines. Ropes & Gray framed the new system as a way to preserve the quality and consistency of legal advice while shifting lawyers’ time from the mechanical act of reading every contract toward higher-level judgment. In the firm’s view, automating the first-pass review can make diligence faster without sacrificing the legal judgments clients pay for.
The firm began development with a narrow focus on material commercial contracts. Practice AI lead and firm partners organized working sessions in which lawyers compared the tool’s draft outputs with human-authored diligence reports from closed matters. Those side-by-side comparisons informed adjustments to the tool’s rubric and the structure of its drafted reports so that outputs better matched the firm’s established standards.
Ropes & Gray designed the workflow to reflect the sequence its teams follow: an initial review, written summaries, and then higher-level analysis. The firm positions the system’s capabilities as comparable to junior-to-mid-level associate work. In practice, the transactional diligence AI performs a consistent, repeatable first pass—grouping documents, highlighting potentially relevant material, and drafting summaries—while leaving final judgments to experienced lawyers.
Functionally, the tool inventories and categorizes a data room so reviewers can see relationships across documents rather than isolated files. It automatically groups documents by themes and issues, highlights materials the model flags as relevant, and generates both interim summaries and a completed report in Ropes & Gray’s signature format. Partners stress that all interim summaries and source documents remain subject to lawyer review before any client delivery.
Josh Lichtenstein, Partner and Head of ERISA Fiduciary Practice, said the technology helps maintain quality and consistency while moving work along more quickly. Howard Glazer, Partner and Head of Practice AI, emphasized that clients pay the firm for legal judgment based on contracts, not for the act of reading them—an argument the firm uses to position the tool as a time-saving complement to human expertise.
In internal testing, Ropes & Gray compared the system’s draft outputs with lawyer-authored diligence reports to identify where the tool matched human work and where it needed refinement. The firm reports the workflow reliably inventories data rooms, produces grouped document sets, and yields summaries and formatted reports that lawyers can validate and refine. Testing across closed deals provided practical feedback that shaped both the tool’s analytical rubric and the presentation of its results.
Ropes & Gray also cited use of GPT‑6 Astra alongside its proprietary system to accelerate problem solving for clients, noting that such models have helped the firm deliver results faster than before. The firm’s approach combines internally developed software with large language models, using each where it best contributes to speed and accuracy while preserving attorney oversight.
From their deployment the firm’s leaders draw two practical lessons. First, experiment broadly with AI to learn its strengths and failure modes; testing a variety of use cases—including non‑legal ones—helps teams understand how to adapt and mitigate risks. Second, keep clients central: design workflows so client benefit remains the primary measure of success, not technological novelty.
Looking ahead, Ropes & Gray expects to expand AI’s role beyond task automation toward building collective knowledge within the 1,500-lawyer firm. The goal is to make expertise from meetings and past projects more broadly accessible across teams, translating time savings and consistency into direct client value rather than mere cost reductions.
Ropes & Gray’s work illustrates how law firms can combine proprietary systems with large language models to streamline repetitive review tasks and free lawyers to concentrate on considered legal judgments. The firm positions the new capability as a consistent first-pass reviewer that enhances, rather than replaces, attorney oversight and decision-making.
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