Tiger Analytics Knowledge Search

As described on AWS Marketplace.

Tiger Analytics’ Knowledge Search is built using AWS Bedrock, Amazon S3, AWS Glue and other native services its designed to make enterprise knowledge instantly accessible through natural language queries. The system retrieves the most relevant information from unstructured sources—including PDF reports, presentations, and documents—using advanced semantic search and embeddings. It then combines the retrieved content with generative reasoning to produce grounded, accurate responses. The solution supports multi-turn interactions, highlights the source references for transparency, and handles domain-specific nuances through configurable prompts and indexing strategies. With capabilities like metadata filtering, chunking optimizations, and document-type adaptability, the RAG system enables scalable deployment across use cases such as policy lookup, research synthesis, SOP retrieval, and knowledge support. It eliminates the need for manual search and helps users access reliable answers faster across fragmented content repositories

Highlighted by the publisher on AWS Marketplace.

Ensures responses are backed by retrieved content, with source references for transparency and trust

Handles diverse formats like PDFs, PPTs, DOCs, and more, and ingests domain-specific content with custom chunking and filtering strategies

Maintains context across follow-up questions, enabling layered, dynamic interactions