TensorStax is built for data engineers who want their pipelines to work. The platform uses autonomous AI agents to plan, make, and maintain production-ready data pipelines, while integrating seamlessly with existing stacks. It works alongside familiar tools like dbt, Airflow, Spark, and more—no rip-and-replace required.
Active Integrations with Tensorstax
TensorStax connects directly with the tools data teams already trust:
- Apache Airflow
- dbt (data build tool)
- AWS Glue
- Apache Spark
- Databricks Notebooks
Trusted by Data Leaders
Designed for the modern data stack, TensorStax integrates with industry-standard tools—from dbt to Spark—to remove manual overhead and help teams move faster with confidence.
Just describe what you need—or pull in existing tickets.
Start with your intent, whether it’s a new DBT model, an Airflow DAG, or a complete ELT flow. You can also bring in tickets directly from your project tracking tools.
Tensorstax = The Agentic Data OS
Your data stack—supercharged.
Self-healing pipelines
(Autofix with Git integration)
The system automatically detects issues, suggests fixes, and creates GitHub pull requests to repair broken code and failing pipelines—before they turn into bigger problems.
Generate data models, tests, and pipelines.
(Auto code completion & validation)
Create dbt models, tests, and assertions directly on your own infrastructure. Everything is generated with strong schema typing, built-in validation, and performance-ready patterns.
Compiler layer
Before anything ships, the agent fills in missing code, validates syntax and DAG structure, and runs dry runs to make sure everything works as expected.
Data OS & observability
Manage and monitor pipelines across all tools from a single, centralized view—so nothing slips through the cracks.
