Speed compliant content at scale
Announced 2025
Status Early
Customers 1-10
Veeva AI for PromoMats enhances how medtech teams review and approve content by combining human expertise with AI precision to strengthen compliance, accelerate speed to market, and increase productivity.
Built natively into the Vault Platform, Veeva AI for PromoMats utilizes two standard agents.
- The Quick Check Agent scans content using editorial, brand, market, channel, and compliance guidelines to address issues before medical, legal, and regulatory (MLR) review.
- The Content Agent provides context-aware insights into document text and images, answers questions, summarizes content, analyzes visuals, and draws from Quick Check Agent to assist with document review. This ensures higher-quality, compliant content-delivered faster with greater confidence.
With your content and data staying securely inside Vault, Veeva AI for PromoMats provides a seamless, compliant experience that scales with your organization. These agents are the initial focus where we will continue to build purpose-built agents to modernize the MLR process and unlock new levels of efficiency.
Veeva AI for PromoMats Impact
Accelerate compliant content delivery
Automated AI checks to speed approvals while ensuring consistent compliance.Increase productivity across teams
Reduce noise and manual effort while empowering teams to focus on higher value work.Transform the MLR process
Modernize MLR with proactive quality checks and more efficient, consistent reviews.
See Veeva AI for PromoMats: Quick Check Agent and Content Agent
Customer Success
Medtechs redefine engagement with Veeva Commercial
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Learn more about Veeva PromoMats

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LivaNova shares how they are reducing compliance risks and streamlining review and approval with Veeva PromoMats

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Veeva PromoMats Features Brief

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AI in Medtech from real world evidence to content generation

Customer story
Fresenius Medical Care reduces commercial content approval by 14 days

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Philips ensures compliance with an effective claims management approach

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Building the Future of MLR with AI: Fastest Path to Approved Content
