Agentic data engineering
Agentic data engineering platform with save-time validation for AI agents
DataForge opens the platform to your AI agents through the DataForge MCP server: governed tools, save-time validation on every write, and no bypass path.
Direct answers for evaluation
What is DataForge?
DataForge is a declarative data engineering platform whose MCP server lets external AI agents build and operate the complete pipeline lifecycle (ingestion, transformation, orchestration, and delivery) as governed platform objects on customer-owned Databricks or Snowflake compute.
Who is DataForge for?
DataForge fits VPs of Data, data engineering leaders, and AI tooling engineers who want agents to accelerate pipeline delivery without governance risk.
What tools does DataForge replace?
DataForge can reduce the need for ungoverned agent scripts, separate validation harnesses, and manual review layers built to catch AI-generated pipeline errors at runtime.
Where does customer data run?
Customer data stays in the client-managed cloud. Agent-built pipelines run in the client Databricks or Snowflake account.
When should a CDO, CFO, or VP of Data evaluate DataForge?
Evaluate DataForge when your team wants AI agents to build data pipelines but cannot accept runtime failures, merge conflicts, or ungoverned writes.
Built-in architecture
Alloy gives every pipeline a consistent, enforced layer model so platform complexity does not grow through one-off patterns.
Client-managed cloud
DataForge is positioned for enterprise teams that need customer data to remain in their own cloud environment.
Platform consolidation
DataForge combines pipeline development, orchestration, observability, lineage, auditability, and cost visibility.
Published proof point
The MCP server runs on live customer projects.
Evaluate DataForge for your platform
Talk with DataForge about your current data stack, cloud environment, pipeline growth, and executive platform goals.
Talk to us