Why Everlaw Is the Best Choice for the Largest, Most Complex Legal Matters
Large litigations and investigations create challenges that go well beyond document count.
Millions of documents must be processed, searched, reviewed, analyzed, and converted into a coherent case strategy. Data arrives in increasingly varied formats. Review teams expand rapidly across firms, offices, and jurisdictions. Costs rise with every unnecessary document, duplicated workflow, and technical bottleneck. At the same time, legal teams must preserve security, governance, auditability, and confidence in every conclusion.
Everlaw brings these capabilities together in a single platform. With proven performance across multi-million-document and multi-terabyte matters, Everlaw helps legal teams move from early data assessment through review, investigation, case development, and trial preparation without sacrificing speed, usability, or defensibility.
Proven at the Scale of Consequential Litigation
Everlaw is trusted by Fortune 100 corporate legal departments, 91 of the Am Law 200, the largest U.S. federal agencies and every U.S. state attorney general to support complex, high-stakes legal work.
That trust is backed by experience across some of the largest and most operationally demanding matters.
Everlaw is adept at operating at exceptional scale. The largest matter in Everlaw, as of publication, included more than 239 million documents, for example, significantly outpacing other platforms.
One Am Law firm easily managed a nine-terabyte case in Everlaw, providing what the firm characterized as solid proof of the platform’s power. In construction litigation, Everlaw regularly supports cases involving five million, ten million, or more documents.
Everlaw also powered collaboration across wide-ranging, complex multi-district litigation such as the General Motors Ignition Switch litigation, where 31 law firms and 200 reviewers worked across 2.5 million documents. In a complex antitrust investigation, a state attorney general’s office managed 1.18 million documents, 80 assignment groups, and 3,440 review assignments. Another state matter involved more than one million documents and 135 reviewers. In one massive federal government matter over 1,000 reviewers concurrently worked in a single database.
Everlaw Deep Dive extends that scale to evidence-grounded generative AI, allowing users to ask natural language questions to develop quick insights across their databases, with proven performance across data sets containing more than 20 million documents.
Compare that to document limits of 50,000 docs or fewer required by alternative platforms.
Deep Dive operates across data sizes 400 times larger than other tools can handle.
Speed – Across Millions of Documents
Performance becomes a strategic issue at large volumes.
A delay of less than two seconds may seem minor when opening a single document. Repeated across millions and it can represent hundreds of hours of lost reviewer time.
Everlaw processes up to one million documents per hour while checking for errors and duplicates. Documents generally load in approximately 0.125 seconds, and searches typically return in under a second, including across data sets containing millions of records.
In one construction litigation case study, Everlaw loaded documents for reviewers 192% faster than the industry standard. The firm calculated that a 1.8-second improvement across one million documents equated to approximately 500 hours saved.
Another Everlaw user described regularly processing a terabyte of data in a day and having it uploaded, indexed, imaged, and fully searchable within 24 hours.
That speed carries through the platform. Teams can run sophisticated searches, review documents, analyze patterns, and execute multiple production jobs without creating queues that slow down other users.
For large matters operating under compressed timelines, this means less time waiting on the technology and more time acting on the evidence.
Designed for Complexity and Collaboration
The largest matters rarely consist of a uniform collection of emails and PDFs.
Legal teams may need to work across spreadsheets, CAD files, Slack messages, multimedia, images, and audio or video transcripts, alongside millions of conventional documents. Everlaw supports 194 unique file types, allowing legal professionals to manage varied evidence in one environment, reducing the need to move data between disconnected systems or create parallel workflows for unusual data formats.
A construction litigation customer specifically cited Everlaw’s ability to support an enormous range of file formats and produce otherwise unsupported types natively when needed.
The platform combines that breadth with search that is accessible to new users and sophisticated enough for experienced practitioners. Teams can build highly complex queries and receive results nearly instantaneously, regardless of the size of the document population.
Predictive Coding, Clustering, email threading, interactive visualizations, and search term reporting are also built into the same environment. Together, these capabilities help teams identify patterns, understand relationships, prioritize likely relevant material, and determine where human attention will have the greatest impact.
Faster Onboarding for Large Review Teams
Standing up a review involving dozens or hundreds of people can become a project of its own.
Reviewers may join at different stages, work from multiple locations, and bring widely varying levels of technical experience. Lengthy platform training creates expense before substantive review has even begun.
Everlaw is powerful for experts while remaining accessible across the broader case team. Partners, associates, paralegals, investigators, contract reviewers, and litigation support professionals can work directly in the same matter.
One firm reported running matters with more than 100 contract attorneys working in Everlaw simultaneously. Because the platform was intuitive, the firm could issue credentials and send reviewers directly into search and review rather than requiring “hours and hours of training.”
Continuity matters in complex litigation. The knowledge developed during review remains connected to the evidence and available to the teams responsible for motions, depositions, expert work, settlement strategy, and trial.
A state attorney general’s office faced a similar need to onboard large groups of reviewers with different technical backgrounds. Everlaw’s speed and usability helped the office bring those users into the matter quickly and manage the review at scale.
This accessibility also reduces dependency on a small group of technical specialists. More members of the legal team can search the evidence, test hypotheses, and contribute directly to the development of the case.
Reduce the Review Population before Review Begins
In large matters, one of the most consequential decisions is determining what does not need to be reviewed.
Everlaw’s built-in Early Case Assessment capabilities help teams understand incoming data and reduce the population promoted to active review. On average, Everlaw ECA users reduce the number of documents promoted to active review by over 70%.
A U.S. state attorney general’s office projected that using Everlaw ECA would reduce spend by more than 20%.
In another antitrust matter, the case team narrowed 1.18 million documents to fewer than 650 key documents while uncovering new investigative leads. That result required more than eliminating duplicates or applying broad date filters. It depended on combining targeted search, analytics, structured review, and collaboration to progressively isolate the evidence that mattered most.
At large scale, effective culling changes the economics of the entire matter. Every document removed before active review reduces downstream review hours, quality-control work, infrastructure demands, and expense.
AI that Stays Grounded Across Millions of Documents
Generic AI tools may help draft or summarize isolated content. Complex legal matters require AI that operates within the governed case workflow, understands the underlying corpus, and supports every answer with verifiable evidence.
Everlaw’s AI tools are embedded directly within the matter. Their outputs are grounded in the evidence, linked to supporting documents, and preserved within an auditable environment.
Deep Dive allows legal teams to ask natural-language questions across millions of documents and receive answers with direct citations to the underlying record. This enables users to investigate nuanced factual questions without first knowing the exact keywords, custodians, or document locations involved.
Coding Suggestions supports first-pass review with recall and precision that rivals eyes-on review. Everlaw users now report finalizing critical review tasks, such as locating and reviewing key litigation evidence, in a matter of hours, replacing manual review plans initially expected to take months – an estimated savings of tens of thousands of hours of manual work.
Carry the Evidence into Case Strategy
Document review is never the final objective alone. Legal teams need to transform the most important documents into timelines, witness preparation, deposition strategy, case narratives, and trial materials.
Everlaw connects discovery and case-building through Storybuilder and related collaboration tools. Teams can organize key evidence, develop factual narratives, test competing theories, and prepare for depositions or trial without exporting their work into disconnected systems.
In the complex consumer protection litigation, legal teams have used Storybuilder to curate 15,700 key documents from a population of 2.5 million. Thirty-one firms and 200 reviewers collaborated remotely and in real time without the version-control problems created by emailed files and separate work-product repositories.
The Indiana Attorney General’s Office used Storybuilder to test multiple case theories early, helping the team avoid spending time on paths that could not be supported by the evidence.
In the state antitrust matter mentioned above, team members were able to centralize strategy and communications within the Everlaw platform, exchanging nearly 5,000 messages in Everlaw over nine months as they refined their investigative approach.
This continuity matters in complex litigation. The knowledge developed during review remains connected to the evidence and available to the teams responsible for motions, depositions, expert work, settlement strategy, and trial.
Governance and Defensibility within Your Workflows
The stakes of a matter often rise alongside its scale.
Large reviews may include sensitive corporate records, privileged communications, government information, personal data, and evidence subject to demanding discovery obligations. Teams need controls that preserve visibility into who accessed information, how review decisions were made, and what evidence supports an AI-generated conclusion.
The value of a large-matter platform is ultimately measured by its effect on outcomes, workload, and revenue.
Everlaw maintains auditability through comprehensive logging, governed workflows, and AI insights grounded in the actual evidence. Deep Dive answers include direct citations, allowing users to verify outputs against the source material rather than relying on an opaque response.
Predictive coding provides performance statistics that help teams evaluate the quality of the model, support defensible review decisions, and understand when the review may reasonably stop.
Everlaw’s security program includes SOC 2 Type 2 compliance, FedRAMP Moderate Authorization, and GovRAMP Moderate Authorization, ISO 27001 (security management for office sites, development, support and data centers), ISO 27017 (security best practice for cloud providers), ISO 27018 (protecting personal data in cloud environments), supporting sensitive work across private-sector and government matters. (Read more about our data security, privacy and compliance at our Trust Center.)
These controls allow teams to adopt advanced analytics and AI without moving evidence into unmanaged external workflows or losing the governance expected in legal practice.
A More Efficient Model for Complex Legal Work
The value of a large-matter platform is ultimately measured by its effect on outcomes, workload, and revenue.
Everlaw customers have reported dramatically reducing their reliance on outside providers, helping reduce costs and grow new revenue streams. One organization cut its total technology spend from approximately $918,000 to $473,000 after moving away from a legacy platform with a managed-service-provider-heavy model, a savings of 48.5%.
These savings are produced across the lifecycle of the matter: faster processing, fewer documents promoted to review, quicker reviewer onboarding, higher review throughput, reduced platform administration, more efficient collaboration, and less duplication between discovery and case preparation.
Performance becomes a strategic issue at large volumes.
For legal teams managing high-stakes, high-volume, and high-complexity work, Everlaw provides more than the capacity to hold the data. It provides the performance, analytics, AI, collaboration, and governance required to turn that data into a command of the facts. The bottom line: Everlaw handles the biggest matters for the largest, most demanding customers.
One Platform for the Full Complexity of the Matter
Large matters test every part of a legal team’s operating model.
Everlaw is built to meet that challenge with cloud-native scale proven across multi-million-document and multi-terabyte data sets; processing, search, and review speeds that remain responsive at volume; accessible analytics and evidence-grounded AI; and integrated workflows that connect early assessment directly to case strategy and trial preparation.
The result is a platform that helps distributed teams review less, understand more, collaborate effectively, and move faster toward the evidence that will shape the outcome.
