Relational Context
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.
Beyond Vector Search
Flat RAG retrieves text. Graph RAG retrieves structure.
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.
Similarity is not understanding.
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.
Hybrid Intelligence
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.
Low-Friction Migration
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.
Start free, scale with confidence
Launch your first graph-powered agent flow quickly, then scale to enterprise throughput with reliability, support, and infrastructure options designed for production teams.
DevelopREST & Python SDK
ExecuteServerless compute
ScaleEnterprise ready
MonitorConsole & alerts