Everlaw Enhances AI with Deep Dive Tool and Gains FedRAMP Certification
Everlaw has announced two significant advancements: the expansion of its AI Deep Dive tool to open beta and the achievement of FedRAMP certification for its AI Assistant features. These developments mark important steps for legal teams seeking efficient ways to manage and analyze large volumes of documents.
Deep Dive Now Open for Wider Use
Previously known as “Project Query,” Deep Dive enables legal professionals to ask complex, natural language questions about extensive document collections and receive detailed, fact-based answers within seconds. The tool not only synthesizes information but also provides full citations and access to the source documents, allowing users to verify the accuracy of the responses.
Everlaw’s CEO demonstrated Deep Dive using a dataset of 1.3 million documents from opioid litigation. The AI quickly identified relevant subsets, extracted key facts, scored their importance, and delivered scientifically grounded explanations complete with document references. For example, when queried about how opioids work, Deep Dive drew explanations directly from the case documents, backed by citations.
Transparency and Verification Built-In
Deep Dive begins by scanning roughly 10,000 documents identified as potentially relevant. It then narrows the focus to about 50 documents that provide the most pertinent information. Each fact presented is linked to specific source documents, enabling users to confirm the basis of every claim.
While the tool aims for transparency, Everlaw acknowledges its limitations. Users are encouraged to verify facts just as they would when relying on input from junior staff. If there isn’t enough evidence to answer a question, Deep Dive will clearly state, “No promising answers were discovered.”
Importantly, Deep Dive is not intended for comprehensive document review or privilege assessments; Everlaw offers other AI tools for those purposes. Instead, Deep Dive serves as a guide to help legal teams uncover insights and inform discovery strategies.
How Deep Dive Works
The tool uses retrieval-augmented generation (RAG) and advanced reasoning models, including OpenAI’s o3 model, to analyze and synthesize information. Everlaw leverages existing AI investments from major providers like Google and OpenAI, focusing its development efforts on effective implementation rather than building foundational models from scratch.
Documents are broken into 200-300 word chunks with overlapping context to maintain meaning. The system typically reviews about 10,000 documents before highlighting the most relevant 50. This limit is intentional to maintain signal clarity rather than a technical constraint.
Practical Use Cases
- Last-Minute Deposition Prep: Quickly analyze new data dumps from opposing parties to prepare for depositions on short notice.
- Early Case Assessment: Explore evidence within documents to shape litigation strategy efficiently.
- Document Review Planning: Use real case content to plan review workflows more accurately.
In private beta, Deep Dive was tested on cases with document counts ranging from 150,000 up to 10 million. Users reported immediate discovery of critical documents previously overlooked during onboarding sessions.
Looking Ahead
Everlaw plans to enhance Deep Dive with improved query planning, conversational threading for follow-up questions, and expanded context analysis. The company anticipates general availability later this year, though no exact date has been set.
FedRAMP Certification for AI Assistant
Separately, Everlaw is set to receive FedRAMP certification for its AI Assistant suite, including tools like Review Assistant, Coding Suggestions, and Writing Assistant. Expected in September, this certification paves the way for federal agencies to adopt Everlaw’s generative AI features, making it the first e-discovery vendor with a fully FedRAMP-authorized generative AI portfolio.
For legal professionals interested in further exploring AI tools and training, resources are available at Complete AI Training.
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