Legal cases, particularly those involving RICO violations, often present an overwhelming array of multimodal data—financial transactions, communication logs, legal filings, and multimedia evidence. Analyzing this data manually is time-consuming and error-prone. Moreover, existing AI tools, while powerful, fall short of addressing the complexity of such cases.
PRISM (Proactive Retrieval & Intelligence System for Litigation Management) solves this challenge by integrating multiple language models—OpenAI GPT, Claude 3.5, and Mistral—within an advanced AI-driven framework. Through Retrieval-Augmented Generation (RAG), hybrid search techniques, and custom fine-tuning, PRISM provides actionable insights tailored to legal professionals' needs.
Note: This repository is for research and demonstration purposes only. No proprietary or real-world data is included.
The complexity of legal data necessitates a multidimensional analytical approach. Tasks such as synthesizing procedural language, summarizing dense legal documents, extracting metadata patterns, and linking multimodal evidence require specialized capabilities. No single model can fully address these challenges, but a multi-model system can achieve robust and comprehensive results.
| Model | Strengths | Limitations | Role in PRISM | Application Example |
|---|---|---|---|---|
| OpenAI GPT | - Proficient in generating creative and contextually rich narratives.- Strong in procedural drafting and explanatory content.- Highly adaptable to general legal prompts. | - Struggles with deep structured reasoning inherent in legal analysis.- Limited in processing technical metadata. | - Produces initial drafts for legal documents.- Enhances retrieval results with nuanced insights.- Generates narrative coherence across datasets. | Drafts a procedural summary connecting financial irregularities in fraud cases based on retrieved evidence and legal context. |
| Claude 3.5 | - Excels at structured reasoning and summarizing dense legal texts.- Effectively identifies key clauses and contextual patterns in legal documents. | - Limited capabilities in metadata analysis and multimodal data synthesis.- Struggles with non-textual technical inputs. | - Summarizes extensive legal documents.- Organizes and validates retrieved data.- Creates structured legal arguments. | Extracts relevant clauses from a 200-page contract, highlighting potential breaches and contextual connections to case law. |
| Mistral | - Optimized for analyzing technical metadata and handling domain-specific queries.- Strong performance in identifying patterns in structured data and logs.- Efficient at handling multimodal input. | - Ineffective at generating creative or comprehensive narratives.- Limited scope for overarching legal reasoning. | - Analyzes technical data, including timestamps, logs, and geospatial metadata.- Identifies hidden patterns and anomalies in structured datasets. | Detects communication patterns among conspirators in a RICO case by analyzing call logs and timestamps for collusion windows. |