Reveal Launches Agentic AI Suite to Automate eDiscovery and Accelerate Case Development
Reveal, a provider of integrated AI-native platforms for eDiscovery and dispute resolution, has announced the launch of Reveal AI, a new agentic artificial intelligence suite designed to bring together search and analysis, document review, insights, actions and case development within a unified environment.
The new offering represents a significant step in the evolution of AI-powered legal technology. Rather than limiting artificial intelligence to individual tasks such as document classification, searching or review, Reveal AI is designed to help legal teams plan and execute more complex workflows with AI operating across multiple stages of the eDiscovery and dispute resolution lifecycle.
The company said Reveal AI combines technologies and analytics that legal professionals have already used, including aji for document review and ASK for fact-finding and analysis. The suite is also designed to expand over time as Reveal introduces additional agentic capabilities across its product portfolio.
Among the first capabilities introduced is agentic case building, which is built on the foundation of ASK and extends its analytical capabilities into autonomous action. The technology is designed to help legal teams assemble case chronologies, identify important facts and prepare deposition materials more efficiently.
Moving Beyond Task-Based AI
Artificial intelligence has already become an important part of modern eDiscovery operations. Legal teams use AI and analytics to process large volumes of documents, identify potentially relevant information, organize evidence and accelerate review.
Reveal’s new approach focuses on moving beyond individual AI-assisted tasks toward agentic workflows.
Traditional AI tools may help a lawyer or legal professional complete a particular action, such as finding documents containing a specific concept or summarizing information. Agentic AI, by contrast, can be designed to understand a broader objective, determine the steps required to accomplish it and execute those steps while remaining subject to human oversight.
Reveal AI is intended to apply this approach to legal case development.
With a plain-language request, the system can plan and perform multiple steps across a matter. It can reason across the available information, identify relevant facts and generate work products while grounding results in cited source documents.
The attorney remains responsible for directing and approving the work, creating a model in which AI can perform substantial amounts of operational work while legal professionals retain control over important decisions.
Agentic Case Building
The first major addition to Reveal’s agentic AI suite is its agentic case-building capability.
Case development can require legal teams to examine large amounts of information from documents, communications and other sources. Lawyers and litigation professionals may need to reconstruct timelines, identify important events, connect facts and prepare materials for depositions and other proceedings.
These activities can be time-consuming when performed manually.
Reveal AI is designed to accelerate these processes by using its analytical foundation to examine information across an entire matter. The system can assemble chronologies, surface key facts and draft deposition-related materials based on information found in the underlying evidence.
Instead of requiring a user to perform each individual step separately, the agentic system can interpret a high-level request and determine the sequence of actions required.
The company said this can reduce the amount of time required to perform case-development work while helping legal teams focus their attention on analysis, strategy and decision-making.
Grounding AI Results in Source Documents
One of the important aspects of Reveal AI is its focus on grounding generated results in cited source documents.
Legal work requires a high degree of accuracy and traceability. Lawyers need to understand where information comes from and be able to verify factual statements against the underlying evidence.
Reveal AI is designed to provide results that are connected to source documents, allowing legal professionals to review the evidence supporting the system’s findings.
This approach is particularly relevant as generative AI becomes more widely used in legal workflows. While AI can process and synthesize large amounts of information, legal professionals need mechanisms for validating AI-generated conclusions.
By grounding results in cited source materials and maintaining attorney oversight, Reveal aims to make agentic AI more practical for legal environments where evidence, accuracy and accountability are essential.
Bringing Multiple AI Capabilities Together
Reveal AI is intended to unify several AI and analytics capabilities under a single approach.
The suite incorporates technologies that legal teams have already used, including aji for document review and ASK for fact-finding and analysis. Combining these capabilities within a broader agentic framework allows Reveal to connect analysis with action.
Instead of treating document review, fact-finding and case development as isolated activities, the platform is designed to support a more connected workflow.
The company plans to continue adding capabilities to the suite in the coming months. One of the planned developments is an agentic interface capable of orchestrating workflows across Reveal’s broader product portfolio.
Reveal’s products include Logikcull, Reveal Enterprise, Onna and Reveal Hold. The planned orchestration layer is intended to connect these capabilities and provide a more unified experience for legal teams.
Choice of AI Models
A central component of Reveal AI’s strategy is giving legal teams greater choice over the AI models they use.
According to Reveal’s 2026 eDiscovery Buyers Report, senior buyers unanimously identified the ability to run proprietary and finely tuned AI models as essential to their eDiscovery operations.
This finding reflects a broader trend in enterprise AI. Organizations increasingly want to determine which models process their information rather than being limited to a single AI model selected by a technology provider.
Reveal AI is designed around what the company describes as three dimensions of choice.
First, organizations can use their own or preferred large language models rather than being restricted to a single vendor’s model.
Second, organizations can determine where Reveal AI operates. The technology can run within Reveal’s commercial cloud or in a customer’s own environment.
Third, customers can adopt AI in a manner that fits their existing organizational processes and technology strategies.
These options are intended to give legal teams more flexibility when implementing AI.
Supporting Different Enterprise Environments
The ability to run AI in different environments can be especially important for organizations with specific security, privacy, compliance or infrastructure requirements.
Some legal teams may prefer to use a commercial cloud environment managed by their technology provider. Others may have established infrastructure or organizational requirements that lead them to deploy technology within their own environment.
Reveal AI is being designed to accommodate both approaches.
This flexibility can help organizations integrate AI without having to completely redesign their existing technology strategies.
For large legal departments, law firms and other organizations handling sensitive information, deployment flexibility can be an important factor when evaluating AI technology.
Connecting Reveal to Frontier AI Applications
Reveal is also preparing to publish MCP connectors across its product suite.
Model Context Protocol, commonly known as MCP, can provide a standardized way for AI applications to connect with external data and tools. Reveal’s planned connectors are intended to allow customers to access Reveal data and workflows directly from AI applications they already use.
The company said this could include platforms such as Claude, ChatGPT and other AI applications.
This approach reflects the increasingly interconnected nature of enterprise AI. Instead of forcing users to work exclusively within one application, organizations are looking for ways to connect their existing AI tools with specialized business systems and proprietary data.
For legal teams, direct access to eDiscovery data and workflows through familiar AI applications could create additional opportunities to integrate AI into daily work.
Planned End-to-End Orchestration
The longer-term vision for Reveal AI extends beyond case development.
Reveal plans to introduce an agentic orchestration layer that can coordinate workflows across its entire product suite.
Under this approach, a legal team could describe what it needs in plain language rather than manually managing every stage of an eDiscovery workflow.
The process could begin with legal hold activities in Reveal Hold and collection through Onna. It could then continue through search, review and case development before moving to production through Reveal Enterprise or Logikcull.
The objective is to create an end-to-end workflow in which AI coordinates multiple stages without requiring users to constantly switch between different tools.
This could reduce manual administrative work and make complex eDiscovery processes easier to manage.
Reducing Workflow Fragmentation
Legal technology environments can involve multiple systems, workflows and specialized applications. Moving information between platforms and coordinating individual steps can create operational complexity.
Reveal’s planned agentic orchestration layer is intended to address this fragmentation by connecting the company’s products within a single AI-driven workflow.
Instead of requiring legal professionals to manage each step independently, the system could coordinate activities based on a single user request.
For example, a team could define the objective of a matter and allow the agentic system to determine the appropriate workflow across preservation, collection, review, analysis and production.
The technology would still operate under human direction, but much of the repetitive coordination could be automated.
Human Oversight Remains Central
Although Reveal is emphasizing autonomous AI capabilities, the company is not positioning the technology as a replacement for lawyers.
Attorney oversight remains an important component of the platform’s design.
Reveal AI can plan and execute steps based on a user’s request, but attorneys are expected to direct and approve the work. This approach recognizes that legal decisions require professional judgment, contextual understanding and accountability.
AI can help process information and perform repetitive analytical tasks, but lawyers remain responsible for determining how evidence should be interpreted and how a case should proceed.
This human-in-the-loop model may be particularly important for organizations considering agentic AI for sensitive legal work.
Enabling Proprietary Workflows
Reveal also plans to support organizations that want to bring their own AI models and encode their own workflows.
This capability could allow sophisticated legal teams to develop proprietary approaches to eDiscovery and case development.
Organizations may have specialized processes, unique datasets or particular analytical requirements that differentiate their operations from other firms or legal departments.
By allowing customers to incorporate their own models and workflows, Reveal aims to provide a platform that can adapt to those differences.
For organizations that view AI as a source of competitive advantage, the ability to develop proprietary workflows could become an important part of their technology strategy.
CEO Highlights the Shift Toward Agentic AI
Eric Harmon, CEO of Reveal, said the legal industry’s next stage of AI adoption involves systems that do more than assist with individual tasks.
According to Harmon, AI has already helped legal teams perform activities such as document review and data analysis. The next step is technology that can plan work, execute it and support the results while operating under the guidance and control of legal professionals.
That philosophy is reflected in Reveal AI’s focus on agentic case development and its planned expansion across the broader eDiscovery lifecycle.
Implications for eDiscovery
The launch comes as legal organizations continue to evaluate how artificial intelligence can improve eDiscovery operations.
The amount of digital information involved in modern disputes can make manual review and analysis difficult and expensive. Organizations are therefore looking for technologies that can help them identify relevant information faster, improve analytical efficiency and reduce the administrative burden placed on legal teams.
Agentic AI could potentially take this process further by coordinating multiple tasks rather than simply assisting with individual actions.
Reveal’s approach is designed to combine existing AI capabilities with autonomous planning and execution, creating a workflow in which the technology can contribute across multiple stages of a matter.
Reveal AI represents Reveal’s broader effort to make agentic artificial intelligence a central component of eDiscovery and dispute resolution workflows.
The initial focus on agentic case building provides legal teams with tools designed to assemble chronologies, surface key facts and prepare deposition materials. Future developments are expected to expand these capabilities into a broader orchestration layer connecting legal hold, collection, search, review, case development and production.
The company’s emphasis on model choice, deployment flexibility and customer-defined workflows is also intended to address the varying requirements of modern legal organizations.
With planned MCP connectors, customers may also be able to connect Reveal’s data and workflows to AI applications they already use, including leading frontier AI platforms.
As the legal industry continues to explore the potential of generative and agentic AI, Reveal is positioning its new suite as a way to move from AI that merely assists with individual tasks toward AI that can plan and carry out complex workflows.
The company believes this transition can help legal professionals spend less time managing repetitive processes and more time on strategy, analysis and legal judgment.
By combining AI-powered document review, fact-finding, search, analysis and case development with an emerging agentic orchestration layer, Reveal AI aims to create a more connected approach to the eDiscovery lifecycle. At the same time, its emphasis on cited evidence, attorney approval, model choice and deployment flexibility is designed to provide organizations with greater control over how AI is incorporated into their legal operations.
The broader goal is to create an AI-native environment in which legal teams can describe what they need in ordinary language and have technology coordinate the work across the eDiscovery process. If Reveal’s planned capabilities develop as outlined, the platform could help reshape how organizations approach preservation, collection, review, analysis and case development while keeping legal professionals firmly in control of the decisions that matter most.
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