ReadingResearchRadarInvestment frameworkAI Company Primary Sources
Sign in / Sign up
← Back to reading
NewsQuick take3 min readDisconfirmAI Editorial

Legal AI Company Legora Says Internal NDA Review Capacity Share Dropped from Approximately 20% to Below 1%

An OpenAI case study on Legora describes how the company's legal team extracted a review playbook from historical contracts, using AI to perform an initial screening of incoming NDAs and flagging terms that exceed acceptable parameters. The General Counsel estimates this process reduced the time spent reviewing NDAs from approximately 20% of the team's capacity to under 1%. The article also covers the engineering team's use of coding agents to explore multiple solutions in parallel. All data are self-reported by the company, with no independent verification.

2026-10-28 17:30 GMT+8

A structural dilemma for legal teams is that there is never enough time, leading to high-value work that should have been completed simply not getting done. Max Junestrand, co-founder and CEO of Stockholm-based Swedish legal AI company Legora, cited this as the company's starting point in a case study released by OpenAI: junior lawyers were asked to review an overwhelming number of contracts overnight, able to focus on only a small fraction, while the rest were either skipped or handled roughly.

Legora's legal team designed a specific mechanism for reviewing NDAs (Non-Disclosure Agreements). Emma Kadri Bergström, the General Counsel, explained that her team first analyzed all NDAs the company had historically signed to extract a review playbook defining which term parameters fall within acceptable ranges. When new NDAs arrive, an AI agent performs an initial screening based on this playbook, flagging terms that exceed acceptable parameters for lawyers to make final judgments. Bergström estimated that before this workflow was introduced, reviewing these tedious documents consumed about 20% of the legal team's capacity; now, that figure has dropped to below 1%.

The key to this mechanism is not that AI replaces lawyers, but that it changes the scope of the review. With limited capacity, teams could only deeply review the most critical few contracts, skipping or quickly scanning the rest. The AI's initial screening allows a large volume of files that would previously have gone unexamined to be processed, thereby expanding the exposure of risk clauses. Alex Fortescue-Webb, Global Head of Legal Engineering at Legora, used due diligence as an example: traditional reviews are constrained by time and cost, forcing teams to concentrate on the most valuable contracts; the Tabular Review workflow can analyze thousands of contracts across multiple questions in minutes, allowing lawyers to apply detailed scrutiny to a much broader range.

Sign in / Sign up
ReadingResearchRadarInvestment frameworkAI Company Primary Sources

The engineering team's approach follows a similar logic of parallel exploration. CTO Jacob Lauritzen stated that coding agents have changed the speed at which new ideas are developed: in the past, the team would research one possible solution, commit engineering time, and then find out if the direction was correct; now, they can explore 20 solutions simultaneously and select the best one within days. In troubleshooting, coding agents can search through millions of lines of logs and track multiple possible explanations simultaneously, sometimes compressing the time from bug report to fix completion to approximately five minutes.

Regarding organizational adoption, the case study lists one lesson: when more than 75% of leaders actively use Legora products, the median usage across the entire organization is three times higher. Lauritzen also noted that the rapid iteration of model capabilities forces him to reconsider the product roadmap every two weeks.

It must be pointed out that all the above data—the shift in capacity from 20% to 1%, the 90% acceleration in contract turnaround, the savings of over $20 million in external litigation and due diligence fees for a single Fortune 100 client, and the return of 9.5 hours per lawyer per week—come from self-statements by Legora management in the case study published by OpenAI. The 20% baseline is an estimate by the General Counsel rather than a system measurement; the $20 million figure does not specify the time frame or calculation method; and the 90% acceleration applies only to one unnamed client. As a success story written by a model provider for a paying customer, positive selection bias is a structural feature. The value of this case lies in providing a description of specific workflow mechanisms, rather than proving any universal performance conclusions.

Sources

  • openai.comOpenAI↗
Continue researching →