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Data Infrastructure That Builds Itself

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Introducing TensorStax
Designed for data engineers, our platform leverages autonomous AI agents to plan, generate, and maintain production-grade pipelines, seamlessly integrating with tools like dbt, Airflow, Spark, and the rest of your stack.
Trusted by data leaders
Engineered for the modern data stack, TensorStax works with the most trusted tools in the industry from dbt to Spark to supercharge your workflows.
01.

Describe what you want. Or pull in tickets

Start by expressing your intent, whether it’s a new dbt model, an Airflow DAG, or a full ELT flow. Or pull in tickets from your project tracking tools.
Title: dbt SFDC
Account Source Model
Update dim_customer dbt model to add subscription_tier and mrr_value fields from SNOWFLAKE.RAW.STRIPE_SUBSCRIPTIONS. Schedule via Airflow DAG customer_refresh to run daily at 6 AM after stripe ingestion. Include data quality tests for null values.
Require Approval
Active Connections:
Snowflakes logo, png
Airflow logotype, png
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Context: 5 Tables
02.

TensorStax
plans it

TensorStax generates a structured plan tailored to your infrastructure, schemas and custom rules you’ve defined
Step 1.
Update dim_customer dbt Model
marts/dim_customer.sql to join with STRIPE_SUBSCRIPTIONS
Add new fields:
subscription_tier, mrr_value with proper type casting and null handling
Update model materialization to incremental with unique_key='customer_id'
Finalize and publish the model
Step 2.
Add Data Quality Tests
Create models/marts/schema.yml tests:
Custom test for mrr_value >= 0
accepted_values test for subscription_tier: ['basic']
Step 3.
Create Airflow DAG for dim_customer.sql
Verify DAG runs successfully
Make a PR for updated DAG
03.

Pipeline generated in seconds

Automated data ingestion, transformation, and orchestration using Agent and Airflow
04.

All outputs are always verified

TensorStax verifies all pipelines by dry running them against your data, this ensures you know whether or not the generated pipeline will work in production or not.
Airflow logotype, png
Airflow DAG Dry Run
In-memory replica of target Snowflake table created.
Simulates insert into
All steps succeeded in dry-run
Upstream tasks resolved
No live infra hit
DBT logotype, png
DBT Model Validation
Model preview against warehouse snapshot
Returns rows from
All dbt tests passed
6/6 schema tests validated
No production data queried

The Agentic
Data OS

Your Data Stack, Supercharged
self healing Pipelines
(Autofix + git integration)
Detects issues, suggests fixes, and automatically creates GitHub pull requests to patch failing code and pipelines.
Generate Data Models, TESTs, PIPELINES On your infrastrucrure
(Auto Code Completion & Validation)
Automatically generate tests, assertions, and new dbt models with strong schema typing and performance patterns.
Compiler Layer
Agent generates missing code blocks, validates syntax and DAG structure, and runs dry-runs to ensure correctness before deployment.
Data OS & OVERVABILITY
Manage your pipelines across all of your tools in one centralized location.

Getting Started Is Easy

1
connect
data
2
define
schema
3
invoke
agent
Step 1.
Connect your data
Choose your source: Snowflake, PostgreSQL, S3, etc.
Configure access permissions.
Step 2.
Define your schema
Map tables and join keys.
Set primary logic for transformations.
Step 3.
Call in the Agent
Agent reads your draft.
Rewrites, optimizes, and suggests changes.
Explains decisions contextually.

Built-in security and compliance

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TensorStax secures every connection, pipeline, and runtime environment with credentials managed in HashiCorp Vault. Credentials are never stored in code or configuration files and are only accessed at runtime, providing security and compliance from integration through deployment.
Secure Connections
Encrypted credentials, secure OAuth and token-based access
Compliance-Ready
SOC2 Type 2
No live infra hit
Dry-runs and model validations run safely in memory
Isolated environments
Containerized runtime per task or pipeline

Built For Modern Data Teams.

Cloud Execution
Launch and track distributed jobs with visual progress, dry-run output, and real-time agent suggestions. All in your cloud.
TensorStax, AWS Glue, image UI
Modeling and Testing
Edit models, auto-generate tests, and validate results with built-in context-aware assistance.
Automated validation, instant testing, zero guesswork.
Request a demo
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Frequently Asked Questions

How is TensorStax different from other pipeline management tools?
TensorStax uses autonomous AI to not just visualize or monitor pipelines, but to actively build, optimize, and maintain them. It integrates directly with your stack (like dbt, Spark or Airflow) and generates production-ready pipelines, with customization and version control out of the box.

TensorStax is also fully aware of your schemas, infrastructure and has context on how how you prefer to structure your pipelines.
Does TensorStax store or access my raw data?
No. TensorStax never stores or accesses your raw data. It operates on pipeline metadata and code, ensuring your sensitive data stays within your infrastructure.
Can TensorStax be deployed in my own cloud?
Yes. TensorStax supports self-hosted deployments in your private cloud or VPC, giving you full control over infrastructure and data governance.
Is TensorStax compliant with enterprise security standards?
Yes. TensorStax follows enterprise-grade security practices, including SOC 2, GDPR, RBAC, and audit logging. We ensure secure data handling and full compliance with industry requirements.
What data platforms does TensorStax support?
TensorStax integrates seamlessly with dbt, Apache Airflow, GitHub, GitLab, and is compatible with most modern data warehouses and lakehouses like Snowflake, BigQuery, Redshift, and Databricks.
How does TensorStax detect and fix pipeline issues?
TensorStax continuously analyzes pipeline logic and runtime behavior. It uses AI to detect errors, anomalies, and anti-patterns, and can automatically suggest or apply fixes through pull requests or in-product suggestions.
Can I customize how TensorStax builds and optimizes pipelines?
Absolutely. You can define custom rules, manually edit AI-generated code, and review everything via GitHub or GitLab PRs. TensorStax gives you full control over how pipelines are created and optimized.
How do I get started with TensorStax?
Getting started is simple. Just click "Request a Demo", and our team will walk you through setup, integrations, and show you how to automate your first pipelines in minutes.
Get Started with TensorStax

Autonomous AI Systems For Data Teams.

Request a demo
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