How Everlaw Brings the Evidence Layer to Legal AI
To understand how legal work is changing, let’s start with the basic mechanics of how lawyers work today.
A brief, a risk analysis, a deposition outline, even a piece of legal advice begins with information: the evidence in the matter, the law that applies to it, and the knowledge the lawyer and their organization have accumulated from prior work.
For decades, technology organized those inputs in separate systems. Evidence lived in discovery platforms. Case law and precedent lived in research tools. Prior work product lived in document management systems and knowledge repositories—or, sometimes, in an email to the team asking whether anyone had ever drafted something on this particular issue in this particular jurisdiction.
But that was as far as technology could go. A human expert was needed to bring it all together. The lawyer found what mattered, synthesized it, applied judgment, and expressed the result as legal work product.
AI Is Changing How Information Gets Integrated
Generative AI changes that model by making word product generation addressable by technology. Now, people can use AI to automate the part of their practice that was most human.
That has an obvious consequence. The number and variety of places where legal work can happen is expanding quickly.
Google, OpenAI and Anthropic are building powerful frontier AI models. Microsoft Copilot is bringing AI into the software where professionals already work. Legal-specific platforms including Harvey, Thomson Reuters CoCounsel Legal, Legora, and others are applying AI to the particular needs of the profession.
Used well, these tools can be extraordinarily powerful.
But intelligence needs information. And in litigation, humans and agents both need the analytical tools to make sense of a very large, very messy evidentiary record.
Ask a model to draft a client update and it may do an impressive job. Ask “What actually happened? What does the record show? Which documents support this argument? What did this person know, and when did they know it?”and the problem changes. The quality of the answer depends on access to the right evidence–and the ability to analyze it correctly.
This is the problem Everlaw solves.
Today, Everlaw announced new integrations with Google’s Gemini Enterprise for Legal, Microsoft Copilot, Harvey, and Thomson Reuters CoCounsel Legal. They join our existing work with Anthropic’s Claude for Legal and Legora.
These companies approach legal AI in different ways. We think that is a good thing.
Our goal is not to decide where lawyers should work. It is to make the evidence they need–and the analytical capabilities required to make use of it–available wherever that work happens.
We call that role the evidence layer for legal AI.
What We Mean by the Evidence Layer
Why does legal AI need a distinct evidence layer? Because litigation and investigations impose a set of requirements that a general intelligence layer should not have to, and simply cannot, rebuild from scratch: ingesting and processing complex data, searching it at scale, preserving permissions and chain of custody, and tying conclusions back to the record.
Everlaw provides both secure access to the underlying data and the tools needed to understand it. Through technologies such as the Model Context Protocol (MCP), external AI platforms can securely access information within Everlaw.
At the same time, Everlaw offers powerful analytical tools designed to help humans—and now AI agents—make sense of massive datasets, evaluate the evidence, and build a defensible understanding of the facts.
Let’s start with what the evidence layer is not. It is not an “intelligence layer.” The intelligence layer is where AI systems integrate sources, apply instructions, and help legal professionals create work product–where “draft a client update on the matter” becomes a hefty email.
The evidence layer is the necessary layer that supports this work, It provides the trusted factual foundation those systems need to produce outputs grounded in the record. But it is not just a data repository.
It does not cede analytical abilities to other AI tools. Rather, it brings the full capabilities of AI built for litigation and investigations, tools proven across some of the largest, most complex, and most societally-impactful matters, directly to the broader AI ecosystem. When the evidence layer already provides these skills reliably, the best architecture is to let the agent use them.
Everlaw’s evidence layer seeks to bring the full power of the Everlaw platform – from the capacity to synthesize evidence across tens of millions of documents to the ability to craft collaborative work product across teams – to any AI platform users may be working in.
It is the underlying, necessary evidentiary foundation that powers legal AI, available wherever legal work happens.
What the Evidence Layer Delivers for Legal Teams
For litigation and investigations, that foundation needs to be solid.
Evidence does not arrive as a tidy folder of documents ready for an LLM. It may mean millions of emails, chats, spreadsheets, PDFs, images, transcripts and unusual file types. It has to be collected, processed, indexed, searched, reviewed, analyzed, coded, redacted, produced and ultimately connected to the factual record of the case.
Everlaw is designed to bring legal teams from raw evidence to facts, insights and strategy without breaking the connection back to the underlying record.
Deep Dive is a good example of this. Deep Dive can answer natural-language questions across massive data sets and return supporting evidence and documents, rather than treating a handful of uploaded files as the full universe of relevant information. So far, Everlaw users have successfully used Deep Dive on databases as large as 20+ million documents without issue–and its limits have still yet to be reached. Through the evidence layer, that expansive capability can be invoked from other tools, so lawyers can access Everlaw’s insights across their tech stack.
That is the basic idea behind the evidence layer: maintain one evidentiary system of record, making it the best at solving the hard problems of handling evidence in litigation and investigations, and let legal teams use those capabilities where they do their work.
Access to Reliable Evidence Should Be Ubiquitous—and Governed
No one knows which AI interface every lawyer will prefer five years from now.
Some legal teams may work primarily in purpose-built applications. Leading firms are already building their own platforms around their particular tools, workflows, and institutional practices.
Some may operate through Microsoft tools. They can bring the evidence layer into the same environment where they already draft and collaborate.
Organizations may build their processes out with AI platforms specifically designed for legal work – Harvey, Legora, and the like. Others still may choose frontier-model providers, such as Anthropic, Gemini, or OpenAI.
Or firms may offer several environments while individual lawyers use whichever matches the task and their preferences.
New interfaces and agents will almost certainly emerge.
Customers should have to predict the winner before they can make a sound technology decision today.
That is why Everlaw is pursuing an open approach to integrating with AI platforms.
Our partnerships with Google, Microsoft, Harvey, Thomson Reuters, Anthropic and Legora are not bets on one intelligence layer over another. They are a bet that legal teams will want choices.
Each partner also contributes their own unique value. A lawyer working in Thomson Reuters CoCounsel Legal, for example, can combine access to the factual record in Everlaw with Thomson Reuters’ legal research and expertise. An attorney operating in Claude Cowork may use Everlaw as part of a customized, agentic workflow. Different platforms bring different models, processes, and agentic capabilities to the same evidence.
A common workflow might begin with Everlaw identifying the factual record across a large collection: the communications showing notice, the documents supporting causation, the chronology of key events or the exhibits most relevant to a witness. An AI system then combines that evidence with legal research, prior work product and the lawyer’s instructions to help draft a motion, prepare an analysis or develop an argument.
In that example, Everlaw finds and analyzes the evidence; another system may combine it with additional information sources and capabilities to help lawyers turn it into work product. The dividing line will not always be exact—and it does not need to be. These are complementary systems.
Opening access to evidence, however, cannot mean giving up control of it.
Legal information comes with unyielding requirements around confidentiality, privilege, access control, and defensibility. Using AI on a matter should not result in uncontrolled copies of sensitive data spreading across an organization, nor should it make it impossible to understand who accessed what and why.
So the evidence layer also has to be a governed layer.
Everlaw remains the system of record beneath these workflows, preserving the security, permissions and auditability legal teams require even as they use a growing range of AI tools. Permissions are maintained across integrations, access controls are enforced, and Everlaw’s security and data protection features remain in place.
That governance framework is fundamental to making the evidence layer effective for the legal profession; evidence has to be available to AI systems without losing the controls that make the resulting work defensible.
Three Ways to Work In, With, or On Everlaw
As this ecosystem evolves, we expect legal teams to interact with Everlaw in three broad ways.
The Native Application: Working Directly in Everlaw
Legal professionals already come to Everlaw every day to collect, review, analyze and produce evidence, develop facts, prepare depositions and build their case.
That is the “in Everlaw” workflow–work directly in Everlaw using capabilities such as Deep Dive, Coding Suggestions, advanced search, and the rest of the litigation and investigations platform. For many teams, this process will not change, despite the growth in other AI platforms.
Maintaining that Everlaw experience is core to the success of our teams and yours and we continue to invest deeply in it.
Every new benefit developed will be felt in Everlaw directly, and through Everlaw, to any partner platforms.
Assisted Intelligence: Bringing the Evidence Layer Into Partner Platforms
The second model is “with Everlaw.” A legal professional works in another AI environment but calls on Everlaw when the task requires the evidentiary record or a capability built around it.
A lawyer might ask an AI tool a factual question, invoke Everlaw search or analysis, receive the relevant information, and keep working without switching applications. Through integrations such as MCP, Everlaw can bring in search, analysis and other capabilities as needed while preserving the connection to the evidentiary record.
Custom Solutions: Building Organization-Specific Experiences
The third approach is “on Everlaw.” Some organizations will build AI tools that are uniquely their own, incorporating Everlaw’s evidence layer as part of the underlying infrastructure.
Firms, technology partners, government agencies, and sophisticated legal departments can use Everlaw as part of custom applications and workflows, building on APIs and MCP connections to keep their tools tied to the same evidentiary system of record.
Why three models? Because different legal teams will choose different ways to work. Most will probably use more than one.
Everlaw should work across all approaches.
The underlying task does not change. Whether a lawyer is reviewing documents directly in Everlaw, asking an AI assistant to investigate a factual question, or using a custom application their firm has built, Everlaw can provide the evidence and analysis underneath the experience.
That is what it means to make the evidence layer available wherever legal work happens.
Building the Evidence Layer That Powers Legal AI
There is a tempting way to respond to every major technology transition: build walls.
Keep the data inside your application. Develop your own version of every adjacent capability. Make leaving the environment difficult enough that customers eventually stop trying.
The current moment calls for the opposite approach.
Legal AI is likely to remain heterogeneous. Different organizations have different security environments, existing technology investments, practice needs and preferences. New tools will emerge. Existing tools will improve. Interfaces will change.
The important constant is the work itself—and the evidence underneath it.
Everlaw holds the factual record for some of the world’s most consequential litigation and investigations. We have spent years building technology for the difficult work that record requires: processing complex data, operating at enormous scale, retrieving information quickly, applying sophisticated analysis, managing productions and maintaining the auditability legal proceedings demand.
Those capabilities become more valuable, not less, when other systems can securely use them.
The Everlaw approach is straightforward. Make Everlaw excellent enough that legal professionals want to work in it, open enough that the tools they choose can work with it, and powerful enough that organizations can confidently build on it.
AI is changing where legal work gets done.
We intend to make sure the evidence can meet lawyers there.
