Unlocking enhanced legal document review with Lexbe and Amazon Bedrock
Legal professionals must analyze massive sets of case documents per matter; keyword searches return unwieldy result sets and fail to surface hidden evidence connections, making manual review time-consuming, error-prone, and costly.
The initial system iteration achieved only a 5% Recall Rate, far below production requirements, and traditional eDiscovery techniques could not surface hidden relationships or produce findings-of-fact reports.
After multiple iterations of collaboration with Amazon, Lexbe Pilot reached up to 90% Recall Rate, enabling polished findings-of-fact reports with source hyperlinks and deep automated inference across large multilingual document sets.
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Frequently asked questions
What did this team achieve with this AI workflow?
After multiple iterations of collaboration with Amazon, Lexbe Pilot reached up to 90% Recall Rate, enabling polished findings-of-fact reports with source hyperlinks and deep automated inference across large multilingu…
What tools did this team use?
Amazon Bedrock, Amazon Bedrock Knowledge Bases, Amazon OpenSearch, AWS Fargate, Amazon CloudFront, Apache Tika, Titan Text v2, Amazon Bedrock Sonnet 3.5, Amazon ECS, Lexbe Pilot.
What results were reported?
Recall Rate - iteration 1 (January 2024): 5%; Recall Rate - iteration 2 (April 2024): 36%; Recall Rate - iteration 3 (June 2024): 60%; Recall Rate - iteration 4 (August 2024): 66% (source-reported, not independently verified).
What failed first in this deployment?
The initial system iteration achieved only a 5% Recall Rate, far below production requirements, and traditional eDiscovery techniques could not surface hidden relationships or produce findings-of-fact reports.
How is this legal document review AI workflow structured?
User submits legal query → Document text extraction → Embedding generation → Semantic retrieval via RAG → LLM response generation → Report delivered to user.