Graph RAG has evolved from simple vector search to graph-native architectures using Knowledge Graphs (KGs) with nodes representing entities and edges representing semantic relationships. This enables multi-hop reasoning and complex contextual answers. However, enterprise practitioners face a micro-decision bottleneck when KGs scale to millions of nodes and edges.

Building, maintaining, and querying large graphs requires probabilistic micro-decisions like determining if "Alphabet Inc." matches "Google LLC" in different contexts. Engineers traditionally use general-purpose autoregressive LLMs (gpt, claude), which add latency and cost while requiring strict prompt engineering. Type Safe AI's Jev offers a "System 1" solution—a non-autoregressive, calibrated decision model that performs typed, probabilistic micro-decisions in parallel with sub-500ms latency at lower cost.

Combining Jev's System 1 decision engine with System 2 autoregressive LLMs creates scalable, low-cost, high-precision Knowledge Graphs and Graph RAG pipelines.