# Introduction

Curiosity Workspace is a product from Curiosity (curiosity.ai) for building data products that combine:

• Graph: model your domain as nodes/edges with explicit schemas.
• Search: make structured and unstructured fields findable with filters and ranking control.
• AI: add embeddings, NLP extraction, and LLM workflows grounded in your workspace data.

These docs are product-first: they explain Curiosity Workspace concepts, operations, and extensibility patterns without depending on any single demo dataset.

flowchart LR
Sources[External_Sources] --> Ingest[Ingestion]
Ingest --> Graph[Graph_Layer]
Graph --> Search[Search_Layer]
Graph --> AI[AI_Layer]
Search --> Apps[Apps_Interfaces]
AI --> Apps
Graph --> Apps
Admin[Admin_Governance] --> Graph
Admin --> Search
Admin --> AI

• Developers: connectors, endpoints, interfaces, integrations, and AI workflows.
• Admins/operators: security, permissions, deployment, monitoring, and environment promotion.

• New to Curiosity Workspace: Getting Started → Overview → Quickstart
• Operating a workspace: Administration → Deployment → Security → Monitoring
• Building on the platform: Core Concepts → Architecture → API Overview

• Search and discovery apps with facets, relevance tuning, and graph navigation.
• Knowledge graph experiences (entity pages, neighbor exploration, contextual filtering).
• Semantic retrieval and hybrid retrieval for long-form text and “similar items”.
• AI-assisted workflows that retrieve context from graph/search before generating outputs.
• Custom internal tools via endpoints and custom interfaces.

• Start with Getting Started → Overview
• Or go deeper on how the product fits together: Core Concepts → Architecture