Global MedTech Manufacturer saves 730 hours annually with AI-powered NDA review automation

In-house legal teams at large MedTech manufacturers routinely absorb a disproportionate share of routine contract work — a structural inefficiency that grows sharper as deal volume increases. At this global MedTech manufacturer, the legal function was fielding a steady stream of third-party NDA reviews from business stakeholders across the organization. While NDAs follow a predictable structure, they require interpretation of company-specific terminology, risk thresholds, and fallback positions — knowledge that couldn't be safely delegated to non-lawyers. The result was legal bandwidth consumed by low-complexity, high-frequency work, crowding out capacity for higher-value matters and creating bottlenecks for business teams awaiting contract clearance.

Unit8 implemented a Generative AI solution built on Large Language Models to automate the initial layer of NDA review. The system compares incoming third-party NDAs against the company's internal terminology guidelines and a 'golden NDA' standard — a pre-approved template representing acceptable contract language. When a third-party submission deviates from the standard, the LLM flags the specific clauses at issue, characterizes the risk introduced by the modification, and generates rewording suggestions aligned with the company's approved positions. This created a self-service workflow for business users: instead of routing every NDA to legal, stakeholders can run an initial review themselves, receiving structured guidance on what to negotiate and how. Legal involvement is preserved for escalated cases where flagged deviations exceed acceptable thresholds.

The implementation delivered an estimated 730 hours of annual time savings for the legal function — time previously spent on routine NDA reviews that can now be redirected to complex negotiations, litigation support, and strategic matters. Beyond the headline figure, the solution restructured how NDA work flows through the organization: business users gained a reliable self-service channel, reducing legal queue depth and accelerating contract turnaround. Key outcomes include:

• 730 hours/year returned to the legal function
• Self-service NDA review enabled for business stakeholders without legal training
• Consistent application of internal terminology standards across all third-party submissions
• Foundation established for expanding AI-assisted review to additional contract types

• Starting with a well-bounded, high-volume contract type like NDAs creates a proof of concept with measurable ROI before committing to broader automation.
• Effective self-service legal tools require more than flagging — pairing risk identification with specific rewording suggestions is what enables non-lawyers to act independently.
• LLM-based contract review works best when grounded in a curated 'golden standard'; the quality of that reference document directly determines output quality.
• Saved legal hours only translate to value if lawyers are redeployed to higher-complexity work — change management matters as much as the technology.