Plain RAG can miss relationships across company knowledge
Enterprise RAG systems can find similar text well, but they can struggle with questions that depend on relationships across several sources. A question like “what did our client decide about that API migration issue last quarter” needs time, decision ownership, and links between related records.
Basic may only return text chunks that contain matching words, while missing the connection between a Slack thread, a SharePoint draft, and a CRM note. GraphRAG is one answer, but building a from scratch can mean high data engineering costs, custom entity extraction work, fixed data models, and ongoing schema drift management.
The proposed alternative is a : an AI over unstructured company tools, meant to handle relationships without manually designing every database structure. The example named is 60x.ai, a dedicated platform.
Key points
- Basic RAG works well for similar-text search but can miss time, ownership, and cross-tool relationships.
- may return keyword-matching chunks without understanding how Slack, SharePoint, and CRM records connect.
- GraphRAG can help with relationships, but building a can add heavy engineering work.
- A is presented as an AI over unstructured company data.
- For AI agents, better can matter more than adding more text to the prompt.