AWS Caseflow AI

Document Page
Case Page
Per Document Analysed View Page
Full Case Analysed Page
Agent for the Case Page

Inspiration

Legal professionals spend countless hours manually reviewing case documents, searching for evidence, and analyzing case patterns. We witnessed firsthand how lawyers drown in paperwork - spending 60% of their time on document review instead of strategic legal work.

When Amazon Bedrock Agents were announced, we saw an opportunity to revolutionize this workflow. We asked ourselves: What if AI agents could read, analyze, and extract insights from legal documents like a team of paralegals working 24/7?

The inspiration came from three key observations:

Document Overload: Law firms handle thousands of pages per case
Repetitive Analysis: Similar patterns appear across cases but require manual identification
Lost Insights: Critical evidence often buried in massive document sets

We envisioned AWS CaseFlow AI - a serverless platform where Bedrock Agents become intelligent legal assistants, transforming how legal teams work with case documents.

What it does

AWS CaseFlow AI is an intelligent legal case management platform built entirely on AWS serverless architecture, powered by Amazon Bedrock's multi-agent system.

Core Capabilities:

1. Intelligent Case Management

Create and organize legal cases with one-click setup
Track case status and document associations
Run comprehensive AI analysis across all case documents simultaneously
Generate professional PDF reports automatically
Email analysis results via AWS SES

2. Smart Document Processing

Upload legal documents (contracts, evidence, statements, filings)
Automatic text extraction and indexing to DynamoDB
AI-powered document analysis using Bedrock foundation models
Auto-generate case timelines from documents
Generate document summaries and key finding extractions
Preview documents with metadata tracking

3. AI Legal Assistant (The Game Changer) Our multi-agent system powered by Amazon Bedrock includes:

Main Orchestrator Agent: Routes queries to specialized sub-agents
Evidence Agent: Analyzes and retrieves evidence from documents
Summary Agent: Creates concise case and document summaries
Draft Agent: Assists with legal document drafting
General Agent: Handles general case inquiries

The AI Assistant is backed by a Knowledge Base with legal domain expertise, ensuring accurate, reliable responses.

4. Real-Time Contradiction Detection

Identifies inconsistencies across multiple documents
Flags conflicting witness statements
Highlights timeline mismatches
Shows confidence scores for detected contradictions

How Legal Teams Use It:

Upload: Drag and drop case documents
Analyze: Click "Run AI Analysis" for instant insights
Query: Ask the AI Assistant questions like "What evidence supports fraud allegations?"
Export: Download comprehensive analysis reports

Result: What took lawyers 20+ hours now takes 2 minutes. Focus shifts from document review to strategic legal thinking.

How we built it

AWS CaseFlow AI is a 100% serverless architecture leveraging 9 AWS services, showcasing the power of cloud-native development.

Architecture Overview:

Frontend Layer:

React + Vite: Modern, fast frontend framework
AWS Amplify: Hosting, CI/CD, and IAM configuration
Responsive design for desktop, tablet, and mobile

Authentication & Security:

Amazon Cognito User Pool: Secure user authentication
IAM roles managed through Amplify
Fine-grained access control on all resources

API Layer:

Amazon API Gateway: RESTful endpoints for all operations
Request validation and throttling
CORS configuration for secure cross-origin requests

Compute Layer (6 Lambda Functions):

Case Management Lambda:
- Creates cases in DynamoDB
- Retrieves case lists and details
Document Upload Lambda (b):
- Handles file uploads to S3
- Extracts text from PDFs, DOCX, images
- Stores metadata in DynamoDB Table B
- Saves extracted content to Table C
Document Analysis Lambda (c):
- Fetches extracted text from Table C
- Invokes Bedrock document analysis model
- Stores results in Table D
Case Analysis Lambda:
- Retrieves all documents for a case
- Sends to Bedrock for comprehensive analysis
- Saves results to Table E
- Triggers PDF generation
Email Lambda:
- S3-triggered when Full PDF report is uploaded
- Sends analysis reports via SES
AI Agent Orchestrator Lambda:
- Receives chat queries from frontend
- Invokes main Bedrock Agent
- Routes to specialized sub-agents

AI/ML Layer (Amazon Bedrock):

Foundation Models: Claude for text analysis
Main Agent: Orchestrates sub-agent actions
Action Groups: 4 specialized agents (Evidence, Summary, Draft, General)
Knowledge Base: Legal domain knowledge with vector embeddings

Storage Layer:

S3 Bucket 1: Original document storage
S3 Bucket 2: Generated PDF reports

Monitoring & Logging:

Amazon CloudWatch: Centralized logging for all Lambdas
Custom metrics for tracking analysis requests
Error alerting and debugging

Technologies & Tools:

Languages: JavaScript (Node.js), (Lambda functions)
AWS SDK: v3 for all AWS service interactions
Frontend: React 18, Vite, modern JavaScript
Text Extraction: Textract for OCR, custom parsers for DOCX

Challenges we ran into

Building a production-ready AI legal platform on serverless architecture presented unique challenges. Here are the key hurdles we overcame:

1. Bedrock Agent Orchestration Complexity

Challenge: Getting multiple Bedrock agents to work together seamlessly was harder than expected. The main agent needed to understand when to route to Evidence Agent vs. Summary Agent vs. Draft Agent.

Solution:

Created detailed action group definitions with clear boundaries
Implemented intent classification in the main agent
Used Knowledge Base to provide context that helps routing decisions
Added extensive prompt engineering to guide agent behaviour

Learning: Multi-agent systems require careful prompt design and clear separation of concerns.

2. DynamoDB Query Optimization

Challenge: Querying documents by caseId was slow as we scaled to hundreds of documents per case. Full table scans were inefficient.

Solution:

Added Global Secondary Index (GSI) on caseId
Implemented pagination for large result sets
Used DynamoDB Streams for real-time updates
Optimized query patterns with composite keys

Impact: Query time reduced from 2-3 seconds to <100ms.

3 Managing State Across Serverless Functions

Challenge: Coordinating multiple Lambda invocations for a single case analysis (fetch docs → analyze → generate PDF → email) was complex.

Solution:

Used Step Functions initially, but too complex
Switched to event-driven architecture with S3 triggers
DynamoDB acts as state management layer
Lambda logs analysis progress in real-time

Simplification: Reduced code complexity by 40%.

4. Contradiction Detection Accuracy

Challenge: Initial implementation flagged too many false positives - statements that seemed contradictory but weren't.

Solution:

Enhanced prompt engineering with legal context
Added confidence scoring system
Implemented human-in-the-loop verification
Used entity recognition to track subjects properly

Accuracy: False positive rate dropped from 65% to 3%.

5. Real-Time Chat Latency

Challenge: AI Assistant responses took 8-15 seconds, frustrating users.

Solution:

Implemented streaming responses (show text as it generates)
Optimized Knowledge Base queries
Added response caching for common questions
Pre-loaded agent context to reduce processing time

Result: Perceived latency reduced to 2-3 seconds with streaming.

Accomplishments that we're proud of Complete Serverless Production System for first time

we built a fully functional, production-ready legal platform using 100% serverless architecture. No EC2 instances, no containers, no servers to manage - just pure cloud-native services. This demonstrates that complex AI applications can run entirely serverless.

Impact: Zero server maintenance

Multi-Agent Bedrock System That Actually Works

Implementing Amazon Bedrock's multi-agent architecture wasn't just checking a box - we created a genuinely useful AI system with 5 specialized agents that collaborate intelligently. The Evidence Agent can find needles in document haystacks, the Summary Agent distills 100-page cases into key points, and the Draft Agent assists with legal writing.

What we learned

Technical Learnings:

1. Serverless is a Mindset, Not Just Technology

We learned to think in terms of events, not servers. Every action is a trigger, every Lambda is stateless, every piece of data flows through the system. This required unlearning traditional server patterns.

Key Insight: Don't try to make serverless work like traditional servers. Embrace the event-driven paradigm.

2. DynamoDB is Fast, But You Must Design Correctly

DynamoDB punishes bad data modeling and rewards good design:

Primary keys must support your access patterns
GSIs are not optional for complex queries
Think about queries before you design tables
Single-table design can be overkill for some use cases

Lesson: We redesigned our schema twice. The second redesign reduced query times by 90%.

3. Monitoring is Not Optional

In a serverless world with Lambdas, DynamoDB tables, and multiple S3 buckets, things fail silently. We learned:

CloudWatch alarms are essential
Structured logging saves debugging time
X-Ray tracing reveals bottlenecks
Custom metrics track business KPIs

So many happened seriously we ran into it n number of times without this we could not done it

4. AI Accuracy is Hard

Training data quality matters more than model size:

Garbage in = garbage out
Domain-specific knowledge bases outperform generic ones
Human verification loops are necessary
Confidence scores help users trust the system

What's next for AWS CaseFlow AI

We're just getting started. Here's our roadmap for transforming CaseFlow AI from a hackathon project into a production legal intelligence platform.

Short-Term for Next 3 Months

1. Advanced Document Understanding

OCR Enhancement: Integrate Amazon Textract for better handwritten document recognition
Table Extraction: Parse and analyze data tables from financial documents
Redaction Tool: Automatically redact sensitive information (SSNs, account numbers)
Multilingual Support: Analyze documents in Spanish, French, German

2. Enhanced AI Capabilities

Precedent Finder: Search legal databases for similar cases using vector search
Statute Citation: Automatically identify and link relevant laws/statutes
Witness Statement Analysis: Compare multiple witness accounts for consistency

3. Collaboration Features

Real-Time Collaboration: Multiple lawyers working on same case (WebSocket support)
Comments & Annotations: Add notes to documents and analysis results
Team Workspaces: Separate cases by team/department
Activity Feed: See what teammates are working on