Galaxy offers an enterprise context management platform that unifies definitions, processes, and meaning into a shared, AI‑readable model. By ingesting data from across an organization’s tech stack, extracting structure, and maintaining a living context graph, it enables teams and AI agents to operate with consistent, trustworthy data without rebuilding context for each application. The solution is used in sectors such as legal, insurance, and finance to improve AI output reliability and reduce duplicated effort.
Funding
Funding not disclosed
Founders
Product
Problem
Organizations often let each application or team interpret data independently, leading to duplicated effort, inconsistent definitions, and unreliable AI outputs, especially in regulated sectors like legal, insurance, and finance.
Solution
Galaxy offers an enterprise context management platform that creates a unified, living context graph from data across an organization’s technology stack. By ingesting source data, extracting structure and meaning, and governing entities, relationships, and business logic in a single shared layer, Galaxy ensures that both AI agents and human users operate with a consistent understanding of data. The platform runs in the customer’s cloud and can be queried directly or extended to power analytics, applications, and scoped AI agents. Continuous synchronization keeps the context up to date as the business evolves, reducing duplicated work and improving the reliability of AI-driven insights.
Target Audience
Primary customers are data‑centric teams in regulated industries—such as legal, insurance, finance, and enterprise software—who need a consistent data model for analytics, AI, and operational applications.
Features
- Automated ingestion of data from diverse sources and construction of a connected context graph
- Centralized definition, governance, and versioning of entities, relationships, and business rules
- Entity resolution and Object 360° views that unify records across systems
- Scoped AI agent support that provides curated, compliant context for model inference
- API and query interfaces for integration with analytics tools, applications, and custom workflows
- Cloud-native, distributed architecture that scales with organizational data growth