The client provides a legal management platform that integrates case management, client intake, analytics, and document generation into a unified system.
The platform enables law firms to automate workflows, improve collaboration, and enhance client engagement throughout the case lifecycle.
The client sought to enhance document intelligence capabilities to improve information accessibility and automate document-driven workflows across its legal platform.
- Implement extractive summarization and entity extraction for prioritized legal document types
- Provide an “Ask a Document” capability to enable contextual document querying
- Establish a scalable foundation to support future enhancements such as sentiment analysis and anomaly detection
- Enable structured tracking, evaluation, and continuous improvement of GenAI outputs
- Deliver a rapid prototype to validate business value and accelerate user adoption
- Ensure strict data privacy, security, and compliance within an AWS-controlled environment
- Enable a unified document data processing and analytics foundation to transform unstructured legal content into actionable, analytics-ready datasets
- Improve accessibility and usability of document data across downstream systems and reporting layers
We designed and implemented a cloud-native Generative AI architecture fully deployed within AWS to support document processing, summarization, and contextual querying.
- Used Amazon Textract to extract structured data from legal documents stored in Amazon S3
- Leveraged Amazon Bedrock foundation models to enable extractive summarization and entity extraction
- Implemented Bedrock Guardrails to enforce responsible AI controls and output moderation
- Developed an “Ask a Document” capability using Bedrock-powered contextual response generation
- Built a prompt engineering management framework using Amazon DynamoDB for prompt storage and versioning, enabling dynamic prompt selection and A/B testing
- Deployed serverless processing pipelines using AWS Lambda and Amazon API Gateway
- Secured credentials and configuration using AWS Secrets Manager
- Enabled observability and prompt evaluation through Langfuse integration
- Implemented infrastructure-as-code using AWS CloudFormation orchestrated via the AWS Cloud Development Kit (CDK) to ensure consistent, repeatable, and auditable deployments
- Architected a layered data platform with ingestion, processing, storage, access, and analytics alongside AI capabilities
- Automated document ingestion into Amazon S3 with event-driven pipelines
- Enforced data governance through RBAC, encryption, audit trails, and compliance controls, ensuring confidentiality and controlled access to document-derived data
All data processing remained within the AWS environment to maintain enterprise-grade security and compliance.
By integrating Generative AI capabilities into its platform, the client enhanced document processing efficiency and user experience.
- Reduced manual data entry through automated form filling and entity extraction
- Improved document comprehension using extractive summarization and contextual querying
- Increased productivity through AI-assisted information retrieval
- Established a scalable AI foundation to support future capabilities such as sentiment analysis and anomaly detection
- Strengthened data security posture by keeping all GenAI workloads within AWS
- Created analytics-ready structured datasets to enable reporting, insights, and decision-making across legal workflows
- Reduced data silos with a unified platform and increased visibility through usage insights
- Strengthened governance with auditable, controlled data pipelines
The solution provides a scalable, AWS-native foundation for continued innovation in AI-powered legal document management.
