An in-memory property graph for connected data
Curiosity’s graph engine keeps all entities and relationships in memory, so queries and traversals scale fast without losing flexibility.
Give Your Data Real Context
Model entities and relationships so your search and AI can reason beyond keywords.
Data Silos Broken
Stop patching together disconnected datasets. A unified graph reveals relationships across all sources.
Context in Every Query
Ensure results reflect meaning, not just string matches. Entity-aware search delivers accuracy.
Future-Proof Models
Evolve your data structure without re-engineering everything. Graph schemas adapt as your business grows.
Model data with flexibility
Nodes, edges, and properties let you represent entities and their relationships directly, not as an afterthought.
Fast enough for real work
Graph data is stored and processed in memory, enabling low-latency queries across millions of entities and links.
Turn text into structured data
Automatic extraction and linking enrich documents with entity references, creating a navigable, queryable layer on top of raw data.
Graph + search, no silos
Blend graph traversal with keyword or vector search. Results can be ranked by both relationships and content relevance.
Keep graphs consistent
APIs and admin tools help ingest, update, and clean data across sources while maintaining graph integrity.
Query, test, iterate
A built-in shell makes it easy to explore your graph, prototype queries, and debug pipelines interactively.
APIs for your use case
Expose graph operations through tailored endpoints, so other systems and tools can consume results directly.
