Everlaw Omni Brings an AI Agent to Litigation and Investigation Work

Omni Agent orchestrates the work

According to Everlaw, Omni builds on its existing Deep Dive feature and adds Omni Agent, which turns a user's question or task into a plan and then carries it out inside the Everlaw platform. A user might ask, for example, "Which executives and department heads knew about the issues involving customer funds?"

Everlaw says the agent first identifies the user's intent, then brings together the platform's native tools, orchestrates them into full workflows, synthesizes the results and formulates an answer. It keeps track of context across follow-up questions, so each exchange builds on the last.

The company lists these abilities for Omni Agent:

• Construct and refine searches using Everlaw Query Language
• Analyze communication patterns with Communication Visualizer
• Investigate issues across millions of documents with Deep Dive
• Sequence the right Everlaw tools for multi-step work
• Synthesize results from across a matter

Everlaw says the agent's capabilities will grow over time to include creating interactive visualizations, interfacing with Storybuilder, managing search term reports and assignments, and even managing productions.

The pitch is aimed at a familiar problem. Everlaw argues that lawyers already have search, analytics and generative AI tools, but must decide which feature to use, when, and how to piece the outputs together by hand. Omni is meant to take over that stitching.

Case Memory builds a shared fact library

The second capability, Case Memory, continuously extracts facts from matter documents and creates a central fact library. According to Everlaw, it structures facts based on user-defined criteria, and the library automatically takes in new information as documents are added to a matter. That library can inform Omni Agent outputs and populate Storybuilder.

Everlaw says this removes manual work such as extracting allegations, normalizing names or building timelines from scratch. Legal teams, it adds, remain in control of which facts they include in their work.

The first application of Case Memory will be in Storybuilder, where teams can use it to automate timelines, issue summaries and evidence-backed work product without rebuilding the facts each time.

Trust and defensibility as design pillars

Everlaw frames Omni around three pillars meant to make its output verifiable. The first is source citation. Unlike general consumer models that run broad web searches and draw on embedded knowledge, Omni stays strictly constrained to the user's specific document set, which the company says is designed to minimize hallucinations.

The second is transparency of process. Omni Agent shows the tools it selected, how it used them, the resulting documents and the final response. Everlaw says audit trails are built in, with records of users, prompts, inputs and outputs, which helps teams document their workflow if it is later scrutinized.

The third is granular citation. Every insight, narrative or summary is backed by a specific document, page or paragraph in the corpus, so users can move from output to evidence and check it before relying on it.

Availability and what it means for users

Omni is currently in early access and already in use at several firms, including Bracewell, Cole, Scott, & Kissane, and Vorys, according to Everlaw. General availability is expected in the coming months. As Omni rolls out, Everlaw customers with Deep Dive-enrolled databases will automatically transition to the Omni experience. Interested customers can contact their account team or request a demo.

For legal teams, the draw is a single entry point that combines search, analysis and fact extraction while keeping a record of how each answer was produced. Everlaw describes this as part of its vision of an AI-native litigation platform. How well it performs in practice has not been shown in the announcement, which includes no performance figures.