Automating Legal Document Processing with Cloud
Services
Capabilities
Challenges
Managing Cloud Costs Efficiently
Balancing performance and budget posed a challenge. The team needed a resource strategy that maximized operational efficiency without incurring excessive Cloud expenses.
Integrating Evolving Technologies
Keeping pace with rapidly evolving Cloud services—and ensuring the robust integration of Document AI—required close attention to compatibility and performance optimization.
Enhancing Collaboration for Data Labeling
Seamless communication and collaboration among stakeholders were paramount to effectively label large datasets, ensure high data quality, and maintain a smooth workflow.
Approach
Ensuring Seamless Legal Document Processing Using Cloud-Based Solutions
Applaudo partnered with a government entity to address key challenges involving cost optimization, technology evolution, and collaborative data labeling, all while boosting efficiency for large-scale legal document handling. By introducing an AI- and Cloud-focused strategy, we sought to streamline file partitioning, provide real-time insights, and maintain trustworthy data outputs.
First, we tackled the challenge of managing Cloud costs while sustaining high performance. The existing systems were prone to budget overruns and resource inefficiencies. Our approach focused on implementing dynamic resource allocation within a secure Cloud architecture. This kept operational costs within limits and ensured consistent, reliable performance under varying workloads.
Next, we faced the rapid evolution of AI technologies, particularly in Google Cloud’s Document AI. Our team stayed ahead of updates by building flexible workflows and employing continuous monitoring. Whenever new Document AI features were released, we conducted quick evaluations and integrated them seamlessly, minimizing any impact on day-to-day operations.
Finally, we addressed collaborative data labeling to support high volumes of legal files. Inconsistent input and disjointed processes led to slow, error-prone data quality checks. Our solution introduced a centralized data labeling environment with real-time feedback mechanisms and streamlined user interfaces. This helped stakeholders work cohesively, reduced labeling errors, and ensured reliable, up-to-date information for split-file generation and classification.
Results
Optimized Cloud Resource Allocation
Implemented an automated Cloud cost-optimization framework, allocating resources based on real-time needs—ensuring reliable performance while controlling expenses.
Advanced Document AI Implementation
Integrated Google Document AI into a secure, scalable Cloud architecture including performance tuning, machine learning improvements, and continuous tech updates.
Streamlined Stakeholder Collaboration
Centralized labeling environment that fostered real-time feedback improving data quality and accelerated the file partitioning process, benefiting all parties involved.
