Legal Intelligence Platform Case Study

Legal Intelligence Platform Case Study

Sedmi Odjel Delivers AI-Powered Legal Research Platform on AWS.

Intro

A Croatian law office specializing in commercial, civil, and administrative law faced significant challenges with manual legal research processes. Identifying relevant court precedents required junior staff to search through thousands of court decisions—a time-consuming process that limited the firm's capacity and responsiveness. Sedmi odjel delivered a generative AI solution leveraging Amazon Bedrock and OpenSearch to automate precedent identification, reducing research time by up to 90% while improving accuracy and case preparation throughput.

About the Customer

The customer is an established Croatian law office founded in 2017, serving clients ranging from individuals to small and medium-sized businesses. Their expertise spans regulatory compliance, corporate law, and litigation, positioning them as a trusted legal advisor within the SMB market segment.

The firm's legal team regularly prepares cases across different courts throughout Croatia, where understanding how specific laws are interpreted depends heavily on analyzing past court decisions from relevant jurisdictions. This research-intensive work formed a critical bottleneck in their operations.

The Problem

The law office's legal research process was entirely manual, creating several operational challenges:

• Time-Intensive Research: Junior legal staff spent 8-10 hours per case manually searching, reading, and analyzing court decisions to identify relevant precedents
• Inconsistent Coverage: Manual processes risked missing precedent-setting judgments that could influence case outcomes
• Limited Scalability: Research bottlenecks constrained the firm's capacity to handle multiple cases simultaneously
• Resource Allocation: Skilled legal professionals spent excessive time on administrative search tasks rather than legal strategy

The firm needed an intelligent solution that could rapidly identify relevant court decisions based on semantic similarity, enabling their team to focus on high-value legal analysis rather than document retrieval.

Why AWS Cloud?

AWS emerged as the ideal platform for this generative AI solution due to several key factors:

Managed AI Services: Amazon Bedrock provides access to foundation models like Anthropic Claude without infrastructure management, accelerating time-to-value.

Integrated Knowledge Base: Native integration between Bedrock Knowledge Base and Amazon OpenSearch enables sophisticated RAG (Retrieval Augmented Generation) architectures with minimal custom development.

Enterprise Security: AWS provides the security controls required for handling sensitive legal documents, including encryption, access management, and compliance certifications.

Serverless Architecture: Managed services reduce operational overhead, allowing the customer to focus on legal practice rather than infrastructure management.

Cost Efficiency: Pay-per-use pricing for Bedrock inference aligns costs with actual usage, providing predictable economics for a professional services firm.

Solution

We implemented a production-ready generative AI platform utilizing AWS managed services optimized for document intelligence and natural language processing.

Frontend & Access

• React.js web application for intuitive user interaction
• Amazon CloudFront for secure, low-latency content delivery
• Amazon Cognito for user authentication and access management

API & Integration Layer

• Amazon API Gateway for secure API management
• RESTful endpoints for document upload and query processing

Intelligence Layer

• Amazon Bedrock with Anthropic Claude for natural language understanding and response generation
• Amazon Bedrock Knowledge Base for RAG implementation
• Amazon OpenSearch Service for semantic search and document retrieval

Storage & Data Management

• Amazon S3 for secure document storage with automatic indexing triggers
• Vector embeddings stored in OpenSearch for similarity search

Monitoring & Operations

• Amazon CloudWatch for comprehensive logging and metrics
• AWS X-Ray for distributed tracing and performance analysis

The solution implements a complete RAG workflow: users upload legal documents which are automatically chunked, embedded, and indexed. When users submit queries, the system retrieves semantically relevant court decisions and generates contextual responses grounded in the retrieved precedents.

Results and Benefits

Research Time Reduction: Legal research time reduced from 8-10 hours to under 1 hour per case, achieving 85-90% efficiency improvement.

Increased Capacity: Case preparation throughput doubled without additional headcount, from 3-4 to 6-8 concurrent cases.

Improved Accuracy: AI-powered semantic search identifies relevant precedents that manual searches might miss, improving research comprehensiveness.

Resource Optimization: Junior staff now focus on legal analysis rather than document retrieval, increasing team productivity and job satisfaction.

Scalable Foundation: Serverless architecture scales automatically with usage, supporting firm growth without infrastructure investment.

Production Deployment: Solution is fully deployed and actively used by legal staff for daily case preparation.

Next Steps

The successful deployment has transformed legal research operations and established a foundation for continued innovation:

Expanded Coverage: Ongoing ingestion of additional court decision sources to broaden precedent coverage across jurisdictions.

Enhanced Analytics: Development of trend analysis capabilities to identify patterns in judicial decision-making.

Multi-Language Support: Potential expansion to support legal research across multiple languages for international cases.

Integration Opportunities: Exploration of integrations with existing legal practice management systems.

Sedmi odjel continues to partner with the customer on optimizing and expanding their AI-powered legal research capabilities, enabling them to deliver faster, more comprehensive legal services to their clients.

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