Intergalactic Data Labs provides forward‑deployed engineering teams that build the data infrastructure powering AI agents and automated workflows. Their platform offers pre‑built connectors for databases, warehouses, SaaS tools, and streams, along with transformation and modeling tools that turn entity relationships into queryable data, while AI agents enable plain‑language queries with cited results. Additionally, the MCP layer lets customers integrate the Galaxy data stack directly with their AI applications.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises often have data scattered across multiple databases, SaaS tools, and streams, with inconsistent schemas and no unified, queryable model. This fragmentation makes it difficult to power AI agents and automated workflows, leading to reliance on engineering bottlenecks and delayed insights.
Solution
Intergalactic Data Labs embeds forward‑deployed data engineering teams directly within a customer’s organization to construct a governed data infrastructure that supports AI agents and workflows. The service provides pre‑built connectors for common databases, data warehouses, SaaS applications, and streaming sources, enabling rapid ingestion of raw data. It then applies semantic transformation and entity resolution to create a context graph where relationships between entities become queryable rather than tribal knowledge. Users can interact with this graph through plain‑language AI agents that return cited, real‑time answers, while REST APIs and role‑based access controls ensure secure, programmatic access and compliance. Built‑in observability tracks query performance, data lineage, and policy decisions, giving teams full visibility and auditability.
Target Audience
Primary customers are mid‑to‑large enterprises that run AI‑driven agents or automated workflows and need a reliable, governed data layer, including data engineering, analytics, and product teams.
Features
- Library of pre‑built connectors for databases, data warehouses, SaaS tools, and streaming platforms
- Semantic data transformation with entity resolution that builds a unified context graph
- Queryable entity relationships that replace ad‑hoc, undocumented data joins
- Natural‑language AI agents that generate instant, cited answers over the graph
- REST API endpoints with shared governance for integration with external AI tools
- Fine‑grained RBAC and tag‑based policies to control visibility of sensitive data
- Observability suite providing health metrics, trace logs, alerts, and searchable audit trails
- Custom engineering engagements for bespoke integrations, migrations, and data products