Beyond Vector Search
Retrieving the right documents isn't enough if your AI provides the wrong answers. When your agent needs to understand relationships between entities—not just find passages—you’ve outgrown vector-only retrieval.
Traditional RAG fails when users ask "why" or "how" entities relate. Perseus upgrades your existing pipeline by adding the structured relational layer that flat vectors cannot deliver.
Embedding similarity finds passages; Perseus finds connections. Understand that Company A acquired Company B or that Regulation Z supersedes Regulation W. We map the relationships that live in a graph, not a vector index.
This isn't a rip-and-replace. Perseus adds a graph retrieval layer alongside your existing vector index. Queries hit both, results are merged, and your AI gains relational context with source attribution on every fact.
Move from flat RAG to structured intelligence in two stages. First, we build your knowledge graph from your existing corpus. Then, we integrate the retrieval API. Better accuracy in 4–8 weeks without losing your current pipeline.
