AWS E-DISCOVERY AND COST-RECOVERY DOCUMENT PROCESSING
A billion-dollar global law firm with over 3000 lawyers operating in over 50 offices across Africa, With practices spanning multiple industries, privacy, and regulatory matters, the firm solves complex legal issues for its broad array of private and public-sector clients.
AWS E-DISCOVERY AND COST-RECOVERY DOCUMENT PROCESSING PIPELINE FRAMEWORK
The Challenge:
With the diversity and complexity of legal matters, it handles for its clients, the firm was faced with a unique e-discovery challenge. It had to address the avalanche of data – as documents in various formats – that it received for each client-matter and be able to perform deep and thorough e-discovery on them to best serve their clients’ interests. They also needed a way to tie these deep data-processing compute cycles to specific client-matters to enable correct and complete cost recovery. There was no pre-packaged, off-the-shelf solution that could tackle these challenges.
Solution:
RDA worked with the Advanced Client Data Solutions (ACDS) team to architect and implement a scalable, modular document processing pipeline framework. Leveraging multiple tools within the AWS platform – including AWS Lambda, EventBridge, CloudTrail, and DynamoDB – the framework enabled the ingest of digital client-matter data and performed document conversions, deep Textract text extractions, and natural language processing on the ingested data.
The outputs were then stored in a structured datastore from where they were then available for further consumption and analyses via APIs by other firm tools. The framework also mapped each data ingest to a specific client-matter ID, thus enabling correct cost attribution and revenue capture.
Outcome:
The Firm anticipates significant cost recovery from an area where it previously did not directly attribute costs to the specific client matter, when the framework is deployed to production use. It also expects a significant, and service-differentiating e-discovery boost with the framework, with the ability to easily extend the framework’s processing capabilities to address document types yet-unknown.
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