Vector Store Implementation & RAG Data Layer

Applogika delivers a production-ready vector data layer for enterprises building Retrieval-Augmented Generation (RAG), knowledge assistants, semantic search, and generative AI applications on AWS. The engagement designs and implements the underlying retrieval architecture required to connect enterprise data securely with large language models. The solution includes document ingestion, chunking strategies, embedding generation, metadata enrichment, indexing, filtering, retrieval optimization, and automated re-indexing.

Depending on workload requirements, Applogika can implement vector search using Amazon OpenSearch Serverless, Amazon Aurora PostgreSQL with pgvector, Amazon S3, AWS Lambda, and Amazon Bedrock Knowledge Bases. Hybrid retrieval techniques combining lexical and semantic search can be configured to improve search relevance. The implementation also addresses scalability, access controls, monitoring, operational maintainability, and integration with downstream Amazon Bedrock applications. The customer receives a validated architecture, implemented RAG data layer, documentation, and knowledge transfer for ongoing operations.