The product provides a managed console for ingesting documents, configuring semantic chunking and embeddings, testing vector and hybrid retrieval strategies, and comparing query rewriting approaches. It includes retrieval evaluation, monitoring, and deployment integrations for teams using vector databases, OpenSearch, and LLM providers such as Amazon Bedrock or open-source models.

Multiple companies across AI, crypto, aerospace, data, and SMB software are hiring for nearly identical RAG pipeline work, suggesting the workflow is becoming production-critical but still labor-intensive. As LLM apps move from prototypes to deployed systems, retrieval quality and observability become recurring operational needs.