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Legal customer storyGlobal technology leader

A major enterprise legal team achieved 75% cost savings and 88% faster results with AI-powered document review

Legal customer story

About the global technology company

This worldwide technology leader develops networking hardware, software, and services that enable organizations to securely connect people, devices, and data across the internet and enterprise networks.
Two people working in an office
  • Years operating:
    40+
  • Lead player in:
    Enterprise networking
  • Industries served:
    Banks, government, manufacturing

Summary

Challenges

  • Document review drove 75% of eDiscovery costs.
  • Traditional review required extensive time, training, and supervision.
  • Legal team had to deliver more value and manage rising litigation expenses.

Solution

  • Launched GenAI-first document classification.
  • Accelerated implementation and testing.
  • Integrated seamlessly with existing workflows.

Results

  • Reduced review costs by 75%
  • Achieved 92% recall accuracy
  • Enabled resources to be reallocated to key tasks

Challenges

  • Enterprise legal team faced mounting pressure to reduce eDiscovery costs while maintaining quality standards
  • Document review represented the largest cost component at 75% of total discovery expenses
  • Traditional review methods required extensive human resources, training, and time-intensive quality control processes

This global technology giant’s legal team confronted the industry-wide challenge of escalating eDiscovery costs, particularly in document review, which accounted for 75% or more of its total eDiscovery expenses. With growing pressure from executive leadership to deliver greater business value and manage costs, the legal department needed innovative solutions to transform their litigation support operations.

Traditional document review workflows presented multiple inefficiencies. Human reviewers required extensive training on case-specific criteria, continuous supervision throughout the review process, and iterative quality control measures to ensure consistency and accuracy. These requirements demanded significant time investments from senior attorneys—to oversee reviewer training, monitor ongoing quality, and manage complex project timelines that often spanned months.

The antitrust litigation that served as the pilot case exemplified these challenges perfectly. Matters with high volumes of data demand substantial human resources and extended timelines that strain both budget and personnel. The legal team recognized that any breakthrough in document review efficiency would have transformative impact across their entire litigation portfolio.

Beyond immediate cost concerns, the team faced the strategic imperative to reallocate legal resources from high-volume, low-value tasks to more strategic functions like litigation planning, legal operations, and internal advisory services. The document review bottleneck prevented attorneys from focusing on higher-value work that could better serve the broader business organization.

The challenge was clear: Find a solution that could maintain or exceed human review quality while dramatically reducing both time and cost requirements, enabling the legal department to operate more strategically and efficiently.

Solution

The global technology leader partnered with OpenText to implement an AI-driven document review workflow using OpenText™ eDiscovery Aviator™ review, transforming traditional eDiscovery operations through intelligent automation.

Products deployed

Services provided

Launching GenAI-powered document classification

OpenText eDiscovery Aviator review eliminated the traditional training and supervision requirements of human document review by leveraging a large language model running in a secure AWS Bedrock environment. The system required only the existing review memo—the same document already prepared for human reviewers—as its prompt. This approach bypassed weeks of reviewer training and ongoing quality control supervision, while delivering consistent classification that met or exceeded traditional human-review benchmarks.

Accelerating implementation and testing

The implementation process focused on rigorous validation analyzing roughly 244,000 documents from a previously completed antitrust matter. Employing a comprehensive testing approach ensured the GenAI workflow could deliver reliable, defensible results that would meet judicial standards and provided concrete metrics for comparison against the original human review.

Optimizing resources

Reducing the cost and duration of document review enabled clients to reallocate legal spend from low-value, high-volume work to strategic functions like litigation planning, legal operations, and internal advisory. It also demonstrated that certain projects can be completed entirely in-house—with the support of AI—to reduce dependency on traditional managed review vendors.

Results

The AI-powered document review pilot delivered transformational improvements across cost, speed, and quality metrics, establishing a new standard for efficient eDiscovery operations.

Reduced review costs by 75%

Document review completion time improved by 88%, compressing the timeline from three months to just a few days. This dramatic acceleration will enable faster case resolution, reduced litigation risk exposure, and more responsive support for operations requiring rapid legal determinations. 

Compressed timelines, reduced labor requirements, and decreased hosting and project management overhead meant that the customer achieved approximately 75% cost reduction compared to traditional human review, with total review expenses dropping from $103,363 to $28,496.

Achieved 92% recall accuracy

OpenText eDiscovery Aviator review achieved 87% recall out-of-the-box, increasing to 92% with minor optimizations—significantly exceeding the original human review accuracy. These quality improvements demonstrate that AI-powered review not only matches human performance but can deliver superior accuracy while maintaining consistency across the entire document population.

Optimized resource allocation

The cost efficiency improvement delivered by OpenText eDiscovery Aviator review enabled reallocation of legal budget toward strategic initiatives and higher-value legal work that better serves business objectives like litigation planning, legal operations, and internal advisory. It also demonstrated that appropriate projects could be completed entirely in-house with the support of AI to reduce dependency on traditional managed review vendors.

Shifting review to GenAI reduced reliance on managed review vendors. It also identified key evidence earlier in the process and enabled resource reallocation to higher-value tasks such as legal strategy, early case assessment, and business counseling. This shift not only increases legal's value to the broader business but also supports the department’s strategic mandate to modernize operations through responsible AI innovation.

How AI powers modern eDiscovery review

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Revolutionize your eDiscovery process with OpenText eDiscovery Aviator review

Learn about OpenText eDiscovery Aviator review, a generative AI document review tool built on LLM technology for eDiscovery workflows.

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