Private Client - Intelligent Discovery & eDiscovery Modernizatio

Overview

Worked with small law firm focused on litigation to design and implement an AI‑assisted discovery and investigation platform to support complex environmental litigation. The engagement focused on moving beyond traditional eDiscovery tools by introducing an internal intelligence system that improves document findability, accelerates research, and reduces low‑value review work during pre‑trial preparation.

The Challenge

The firm handles large‑scale litigation involving environmental contamination, where pre‑trial discovery requires reviewing vast volumes of heterogeneous digital evidence. Traditional discovery workflows were time‑intensive, heavily manual, and constrained by tools that emphasized bulk processing over contextual understanding.

Key challenges included:

• Rapidly organizing and searching large discovery datasets sourced from public and government repositories
• Reducing time spent on low‑value document sifting while preserving attorney judgment and strategy
• Establishing a repeatable, secure workflow that could be reused across matters and future datasets
• Maintaining strict data confidentiality requirements, including data sourced from government entities

Solution

To deliver a phased, intelligence‑driven discovery solution, beginning with a prototype and evolving into a pilot system suitable for ongoing use. Key elements of the solution included:

Prototype and Pilot Development

Designed and delivered an initial proof‑of‑concept eDiscovery system using representative datasets to demonstrate how structure, enrichment, and search relevance could be achieved before scaling to sensitive case data.

AI‑Assisted Discovery Functions

The solution introduced targeted “intelligent functions” to assist with discovery tasks such as data organization, relevance exploration, and contextual search—positioned as a force multiplier rather than a replacement for legal expertise.

Repeatable Architecture for Future Matters

The system was explicitly designed to support additional data loads and future enhancements with minimal rework, enabling client to reuse the platform across cases and progressively expand functionality.

Secure Collaboration & Knowledge Transfer

MC+A worked closely with client through regular syncs and technical walkthroughs to ensure transparency, validate outputs, and support internal understanding of the system and its limitations.

Technologies Used

• Elasticsearch Serverless
• Haystack AI
• Crew AI
• Vision models
• Apple Vision Framework
• Text Embeddings & Vision Embeddings

Results

• Improved OCR accuracy, particularly in hand written notes.
• Developed methods for document deduplication.
• Developed framework for ongoing automated objective coding