Building retrieval-augmented generation (RAG) systems for AI agents often involves using multiple layers and technologies for structured data, vectors and graph information. In recent months it has also become increasingly clear that agentic AI systems need memory, sometimes referred to as contextual memory, to operate effectively.The complexity and synchronization of having different data layers to enable context can lead to performance and accuracy issues. It's a challenge that SurrealDB is looking to solve.SurrealDB on Tuesday launched version 3.0 of its namesake database alongside a $23 million Series A extension, bringing total funding to $44 million. The company had taken a different architectural approach than relational databases like PostgreSQL, native vector databases like [...]
Enterprise data teams moving agentic AI into production are hitting a consistent failure point at the data tier. Agents built across a vector store, a relational database, a graph store and a lakehous [...]
A core element of any data retrieval operation is the use of a component known as a retriever. Its job is to retrieve the relevant content for a given query. In the AI era, retrievers have been used a [...]
By now, enterprises understand that retrieval augmented generation (RAG) allows applications and agents to find the best, most grounded information for queries. However, typical RAG setups could be an [...]
Enterprise AI has a data problem. Despite billions in investment and increasingly capable language models, most organizations still can't answer basic analytical questions about their document re [...]
It has become increasingly clear in 2025 that retrieval augmented generation (RAG) isn't enough to meet the growing data requirements for agentic AI.RAG emerged in the last couple of years to bec [...]
Data teams building AI agents keep running into the same failure mode. Questions that require joining structured data with unstructured content, sales figures alongside customer reviews or citation co [...]
By now, many enterprises have deployed some form of RAG. The promise is seductive: index your PDFs, connect an LLM and instantly democratize your corporate knowledge.But for industries dependent on he [...]
A new open-source framework called PageIndex solves one of the old problems of retrieval-augmented generation (RAG): handling very long documents.The classic RAG workflow (chunk documents, calculate e [...]