DeepJudge and Epiq Scale AI Across Law Firms

On April 21, 2026, DeepJudge and Epiq Advisory for Law Firms announced a partnership to scale AI across law firm institutional knowledge, combining DeepJudge's intent-based enterprise search and AI workflows with Epiq's strategic planning, knowledge management, and implementation capabilities. The partnership targets permission-aware, agentic AI workflows that surface relevant insights across matters, support drafting, and embed precedent-driven work into firm processes. "DeepJudge helps firms put the full breadth of that knowledge to work through AI grounded in their own prior work and institutional context," said Paulina Grnarova, CEO and Co-Founder of DeepJudge. "DeepJudge enterprise search and AI workflows enable firms to find and use their institutional knowledge in governed, permission-aware ways," added Jim Tuvell, Managing Director of Epiq Advisory for Law Firms.

The offering centers on three stacked components that firms need to operationalize AI at scale: an intent-based search layer, workflow orchestration for agentic AI use cases, and governance controls for permissions and provenance. DeepJudge provides intent-based search and AI-driven workflows that index documents and surface matter-level insights in real time. Epiq contributes knowledge-management frameworks, governance and adoption programs, and change-management playbooks to translate PoCs into firm-wide adoption. Key capabilities being emphasized include:

• •intent-driven retrieval across documents, precedents, and matter metadata
• •agentic, permission-aware workflows that can support drafting and other document workflows
• •governance and permissions design to support client confidentiality and compliance

Law firms have been experimenting with generative AI for legal drafting and research, but adoption stalls when models are disconnected from firm-specific precedents, metadata, and governance. This partnership addresses that gap by anchoring AI outputs to a firm's prior work product and established KM practices. For practitioners, the value is twofold: higher precision in retrieval and outputs because the models operate over trusted, indexed institutional data, and lower deployment risk because Epiq layers in governance and adoption programs. The move aligns with a broader legaltech trend of coupling specialized AI platforms with consultancy wings to drive measurable ROI rather than islanded pilots.

Firms that pursue this combined route should expect integration work across document stores, practice management systems, and ethics/security review. Technical priorities will include robust metadata mapping, access control enforcement at index and API layers, and evaluation frameworks that measure task-level accuracy plus compliance metrics. Implementation teams should set up benchmarked retrieval tests and human-in-the-loop validation for high-risk drafting tasks.

Adoption metrics and early case studies will determine impact; track whether the partnership produces measurable time savings, higher reuse of precedent, and reduced risk in client work. Also watch for extension to specialty areas like e-discovery, M&A, and regulatory work where precedent and metadata are especially valuable.

This is a pragmatic, market-aware partnership that addresses the two core blockers to legal AI scale-up, namely trusted data integration and governance plus the operational muscle to deploy and adopt workflows across a firm. For legal AI practitioners, it signals a maturing market where platforms must ship capabilities and change-management partnerships to move from pilots to production.