Evisort, a Workday company, has been named a Visionary in the 2024 Gartner® Magic Quadrant™ for Contract Life Cycle Management (CLM) for the third consecutive year.

We’re grateful for this recognition of Evisort’s “completeness of vision” and “ability to execute,” and believe it reflects our team's ongoing commitment to building industry-leading AI into every layer of our product since 2016.

Over the past year, Evisort has continued to innovate and evolve for the benefit of our customers. We’ve released major enhancements to our platform to meet our customers where they are and how they work. We’ve reinforced our leadership as the most trustworthy CLM solution by becoming the first to earn an accredited ISO 42001 certification.

Earlier this month, we became part of Workday, bringing our powerful technology to a leading enterprise platform used by more than 10,500 organizations — from medium-sized businesses to more than 60% of the Fortune 500.

Let’s look at a few more of the ways Evisort has evolved over 2024 to provide our customers even faster time to value and even greater control over their contract processes and their AI journey.

We’ve consistently heard from customers that customizability is key. Teams want a solution that can capture the critical contract data that matters to their particular organization. 

So this year, we introduced Document X-Ray™ and Ask AI, two breakthrough AI features designed to empower businesses with easy access to actionable insights about the terms within their contracts. 

Ask AI lets users ask anything about their contracts in simple, natural language and get clear, reasoned responses — linked to the sources of the answers so they can act with confidence. 

Document X-Ray turns those questions into custom AI models that generate standard data points across all relevant documents so teams can track everything that matters to their organization.

In August, we announced enhancements to our proprietary AI engine, including new, first-to-market AI orchestration functionality that makes Document X-Ray even more powerful and easier to use. 

Before publishing any new AI model in Document X-Ray, the AI engine now runs hundreds of experiments in seconds to optimize performance. It selects the best large language model (LLM) for each specific request from Evisort LLM and other best-of-breed generic LLMs leveraged directly in our platform.

This patent-pending AI orchestration technology also recommends optimized language for each AI prompt to achieve the most accurate results, as gauged by a simple user feedback experience. This innovative use of AI ensures that Evisort users can always gain reliable, customized insights from their contracts without needing advanced prompting skills or special training.

At Evisort, we prioritize security and responsibility and are devoted to providing our customers with the most trustworthy CLM solution on the market. 

As of this writing, we are proud to be the only CLM—and one of the first companies in the world—to achieve an ISO 42001 accredited certification for responsible AI. This certification underlines our commitment to protecting your data and delivering the most accurate, reliable, and transparent AI in our space. 

This new certification complements our existing ISO 27001 (data security) and ISO 27701 (data privacy) certifications. Evisort is the only CLM provider to successfully achieve this combination of critical standards for responsible AI and data protection.

To learn more about the latest evaluation of Evisort by Gartner, download the 2024 Gartner Magic Quadrant for CLM report.

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GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, MAGIC QUADRANT is a registered trademark of Gartner, Inc. and/or its affiliates and is used herein with permission. All rights reserved.

Gartner, Critical Capabilities for Contract Life Cycle Management, 14 October 2024, Lynne Phelan, et. Al.

Gartner, Magic Quadrant for Contract Life Cycle Management, 14 October 2024, Kaitlynn Sommers, et. Al.