Reveal's GenAI eDiscovery Engine: aji, ASK, and AI-Powered Document Review

Legal and compliance teams face a compounding challenge: data volumes grow faster than review capacity, while regulatory timelines compress and litigation risk intensifies. Unstructured data, including the kind generated by email, messaging platforms, and cloud collaboration tools, is expected to triple between 2023 and 2026, and much of that data will fall under legal hold or regulatory scrutiny.

Traditional review workflows, which depend on linear human review and keyword search, cannot scale to meet this demand without significant cost and accuracy tradeoffs. The problem is not just speed: keyword-only search is structurally unreliable. The Blair & Maron study, cited in the TREC 2006 Legal Track overview, found that while attorneys believed they had recovered approximately 75% of relevant documents, the actual recovery rate was closer to 20%. That gap represents real legal exposure.

AI eDiscovery addresses this by applying machine learning, natural language processing, and generative AI to review workflows. For legal operations and compliance professionals, the question is no longer whether to adopt AI-assisted review. It is which platform offers the most defensible, auditable approach for their specific environment.

What Is Reveal's GenAI eDiscovery Engine?

Reveal's GenAI eDiscovery platform is built around three interconnected capabilities:

• aji: Reveal's proprietary generative AI layer for document review and analysis
• ASK: A natural language query interface for direct interrogation of document sets
• AI-Powered Document Review: A structured workflow layer applying predictive coding, concept clustering, and relevance scoring at scale

Together, these components allow legal teams to move from reactive, volume-driven review to proactive, insight-driven investigation.

How Each Component Works

aji: Generative AI for Document Analysis

aji is Reveal's generative AI engine, designed specifically for the demands of legal document review. Unlike general-purpose large language models, aji is built to operate within the governance and confidentiality requirements of legal matters.

Key functions include:

• Document summarization: aji condenses lengthy documents into structured summaries, allowing reviewers to assess relevance without reading every page.
• Issue tagging and classification: The system identifies and labels documents by legal issue, custodian relevance, privilege status, and other matter-specific categories.
• Chronology extraction: aji identifies dates, events, and sequences within documents to assist with timeline construction during investigation.
• Drafting assistance: aji can generate initial privilege log entries, issue descriptions, and document notes, which attorneys then verify and approve.

Reviewer fatigue and inconsistency are among the most significant sources of error in large-scale review. When a generative AI layer handles initial summarization and classification, human reviewers spend their time on judgment-intensive decisions rather than reading repetitive or clearly non-responsive materials.

ASK: Natural Language Querying of Document Sets

ASK is Reveal's conversational interface for document exploration. Rather than constructing complex Boolean keyword strings, reviewers and attorneys can query document collections in plain language.

Example queries:

• "Show me all communications between the CFO and outside counsel after March 2022."
• "Which documents reference the acquisition and also contain financial projections?"
• "Find emails where concerns about product safety were raised internally."

ASK interprets queries semantically rather than syntactically, meaning it retrieves conceptually relevant documents even when they do not contain the exact words used in the query. This directly addresses the recall problem identified in the Blair & Maron research noted above.

AI-Powered Document Review: Workflow Integration

Reveal's AI-powered document review layer operationalizes machine learning within the review workflow itself. Core capabilities include:

• Technology-Assisted Review (TAR): Also called predictive coding, this process trains a model on attorney decisions and applies those decisions consistently across the broader document set.
• Concept clustering: Documents are grouped by semantic similarity, allowing reviewers to work through conceptually related materials together rather than in random order.
• Near-duplicate and email thread analysis: The platform identifies and groups near-duplicate documents and organizes email threads to eliminate redundant review.
• Continuous Active Learning (CAL): The model updates in real time as reviewers make coding decisions, refining predictions throughout the review rather than requiring a separate training phase.

Common Challenges These Tools Address

Challenge

• High document volume, short timelines
• Reviewer inconsistency across large teams
• Documents missed due to keyword reliance
• Privilege review bottlenecks
• Difficulty constructing factual timelines
• High cost-per-document in linear review

How Reveal's GenAI Engine Responds

• AI prioritization surfaces likely-relevant documents first
• Machine learning applies consistent coding logic at scale
• Semantic search and concept clustering capture vocabulary variation
• aji auto-identifies privilege indicators for attorney confirmation
• aji extracts and sequences key dates and events automatically
• CAL reduces the volume of documents requiring full human review

Practical Use Cases

Internal Investigation

A financial services firm conducting an internal investigation into potential accounting irregularities uses aji to summarize thousands of email threads between finance and executive leadership. ASK queries surface communications referencing specific transactions and dates. The review team concentrates human attention on aji-flagged documents, reducing first-pass review time significantly.

Regulatory Response

A healthcare organization responding to a government inquiry uses Reveal's TAR workflow to build a predictive model from a seed set of attorney-reviewed documents. The model applies relevance scores across 1.2 million documents, allowing the team to prioritize the highest-scoring materials and make defensible production decisions faster than manual review would allow.

Litigation Readiness

A corporate legal operations team implements Reveal as a standing eDiscovery platform to manage ongoing litigation. ASK allows in-house counsel to query active matters without depending on outside counsel for every document search, reducing outside counsel fees and response time.

Key Takeaways

• Reveal's GenAI engine combines aji (generative AI for summarization and classification), ASK (natural language document querying), and AI-powered review workflows into a single integrated platform.
• These tools reduce dependence on keyword search, improve reviewer consistency, and lower the cost of large-scale document review.
• Purpose-built legal AI differs materially from general-purpose generative AI: Reveal's tools are designed for the confidentiality, auditability, and defensibility requirements of legal proceedings.
• Document review accounts for approximately 73 cents of every dollar spent on eDiscovery production, according to the RAND Corporation's "Where the Money Goes", making AI-assisted review the highest-leverage cost reduction available to legal teams.
• The combination of CAL, semantic search, and generative summarization is particularly effective for investigations and regulatory responses involving high document volume and compressed timelines.

Ready to See It in Action? Legal operations and compliance teams evaluating how to modernize their document review workflows can schedule a live demonstration of Reveal's GenAI eDiscovery engine. Contact us to schedule your demo.