Logo

litigation

“High Risk Screening of Millions of Documents for Privilege review, and Deposition Cross Referencing”

Actions Used:

🔬

Critique-Based Learning

Analysis
💡

Explanation-Driven Review

Analysis
🤝

Reinforcement from Demonstration

Automation
🕸️

Agentic Graph Retrieval

Analysis

Model Type:

Self-hosted Meta/Mistral/DeepSeek for Document Screening and Cross Referencing. Custom Embedding Models for Cross Referencing.

Problem

A prominent litigation law firm is tasked with reviewing massive document sets to determine which materials are privileged, which are suitable for disclosure, and which are inadmissible. In complex cases, review volumes exceed 40 million documents. Errors in this process can compromise entire cases, leading to sanctions, adverse rulings, or loss of client trust.

During depositions, multiple exhibits and references must be navigated in real time. Existing search and retrieval tools often fail when several related documents need to be identified quickly — especially when key materials are referenced but not explicitly listed. This results in missed connections, overlooked “smoking gun” evidence, and slowed response times in high-stakes moments.

Both workflows are time-intensive, high-risk, and demand extremely high precision. Manual processes are expensive and error-prone. Generic automation approaches fall short on accuracy, explainability, and data privacy.

Simple Solution (Generic)

For privilege review, a standard LLM could be prompted to assess each document’s relevance or confidentiality status. However, LLMs struggle to enforce precise legal rules, particularly when dealing with fragmented or out-of-context material. Their reasoning is opaque, non-verifiable, and prone to hallucination — introducing unacceptable risk in high-stakes legal contexts. Moreover, using external providers exposes sensitive legal content and personal information to third parties, breaching confidentiality requirements.

For deposition support, a naïve Retrieval-Augmented Generation (RAG) pipeline could surface related documents based on simple keyword or embedding similarity. Yet this flat vector-based approach lacks nuance. It misses key materials when language is indirect, and fails to reason across multiple documents or reconstruct evidentiary chains.

Tromero's Solution

Secure. Adaptive. Verified.

Tromero replaces brittle AI pipelines with an architecture designed for legal-grade reliability and performance. It blends symbolic reasoning with explainable generation, and operates entirely on private infrastructure.
1. Multi-Agent Document Review System
Adaptive triage across 40M+ documents, fully private
Tromero’s system orchestrates multiple agents to analyse documents based on legal context. Low-uncertainty cases are classified automatically. Borderline cases are flagged and routed to human reviewers, with full traceability. All processing is performed on-premise, eliminating any risk of data leakage.
Privileged documents are reviewed in a closed loop. Only edge cases are escalated, reducing human workload while preserving control.
2. Human-in-the-Loop Rule Learning
Explainable, testable, optimised legal logic
Tromero learns legal relevance rules through expert feedback and applies reinforcement learning from demonstration. It builds structured, auditable rulesets that outperform static prompt-based systems. Only validated logic is deployed into production.
Rules are iteratively refined and deployed only after exceeding internal accuracy thresholds — ensuring legal defensibility.
3. Explanation-Driven Review Layer
Rationales for every decision — no black boxes
The system justifies classifications using structured reasoning chains. This enhances trust and enables rapid auditing. In sensitive workflows, it ensures that automation is supportable, not speculative.
Each flagged document includes an explanatory trace, showing precisely how a decision was reached.
4. Graph-Based RAG for Deposition Retrieval
Contextual document discovery at deposition speed
Tromero replaces keyword search with Graph-RAG — a logical retrieval system that links concepts across disconnected documents. It reasons through references, even when indirect or incomplete, and surfaces hidden but legally significant connections.
During live depositions, the system identifies and retrieves relevant but unlisted documents by dynamically expanding the evidence graph.

Outcome