LAKE: Living Automated Knowledge Engine

Problem

Poor metadata management severely limits RAG accuracy in knowledge bases. Amazon Bedrock Knowledge Bases requires customers to prepare metadata.json files for all documents and column-specific metadata for tabular data. These labor-intensive requirements often lead customers to abandon proper metadata management, resulting in degraded accuracy and forcing reliance on expensive high-performance models to compensate.

Solution

LAKE provides automatic metadata generation with one-click deployment for S3-based knowledge sources. This agent analyzes files and generates appropriate tags based on user-defined parameters, eliminating manual metadata creation. This AI-powered librarian continuously organizes knowledge sources with minimal human intervention and propose optimized metadata structure based on stored files and user's prompts proactively.

Impact

LAKE's concept is elegantly simple—automatically generate metadata when files are uploaded—yet delivers transformative results. This innovation emerged from real customer cases, proving its effectiveness: 40% increase in RAG accuracy and 60% reduction in knowledge management costs. By removing technical barriers, LAKE makes high-quality RAG accessible to all customers.

Quick Start

Deploy LAKE to your AWS account with one click: