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Welcome to aiden

Transform your data using conversational prompts with our AI-powered data transformations.

Getting Started Clone on GitHub

Revolutionize your data transformation

Transform your data workflows with AI-powered conversational prompts and production-ready code generation.

Natural Language to Pipeline
Transform your data using conversational prompts. Simply describe what you want, and Aiden builds the transformation pipeline for you.
Schema-aware Intelligence
Define your input and output schemas, and Aiden intelligently validates and adapts its logic to ensure your transformations are accurate.
Auto-generated, Deployable Code
Aiden builds production-ready code based on your schema and transformation description, ready to be integrated in your data workflows.

Python Console

>>> from aiden import Transformation
>>> from aiden.common.dataset import Dataset
>>> from aiden.common.environment import Environment
 
# Define input and output datasets
>>> in_dev_dataset = Dataset(
path="./tests/input_data/emails.csv",
format="csv",
schema={"email": str},
)
 
>>> out_dev_dataset = Dataset(
path="./tests/output_data/clean_emails.csv",
format="csv",
schema={"email": str},
)
 
# Create environment object with custom workdir
>>> env = Environment(
type="dagster",
workdir="./tests/workdir/",
)
 
# Define transformation with natural language intent
>>> tr = Transformation(
intent="clean emails column and keep only valid ones.",
environment=env,
)
 
# Build the transformation with specified datasets and providers
>>> tr.build(
input_datasets=[in_dev_dataset],
output_dataset=out_dev_dataset,
)
 
# Deploy the transformation
>>> tr.save("./path/to/artifacts/email_transformation.py")

Python Console

>>> from aiden import Transformation
>>> from aiden.common.dataset import Dataset
>>> from aiden.common.environment import Environment
 
# Define input and output datasets
>>> in_dev_dataset = Dataset(
path="./tests/input_data/emails.csv",
format="csv",
schema={"email": str},
)
 
>>> out_dev_dataset = Dataset(
path="./tests/output_data/clean_emails.csv",
format="csv",
schema={"email": str},
)
 
# Create environment object with custom workdir
>>> env = Environment(
type="dagster",
workdir="./tests/workdir/",
)
 
# Define transformation with natural language intent
>>> tr = Transformation(
intent="clean emails column and keep only valid ones.",
environment=env,
)
 
# Build the transformation with specified datasets and providers
>>> tr.build(
input_datasets=[in_dev_dataset],
output_dataset=out_dev_dataset,
)
 
# Deploy the transformation
>>> tr.save("./path/to/artifacts/email_transformation.py")

Simplify your data transformations

Make every data pipeline efficient with AI-powered transformations and intelligent code generation.

Natural Language to Pipeline
Transform your data using conversational prompts that understand your intent.
Schema-aware Intelligence
Define your input and output schemas, and Aiden intelligently validates and adapts its logic.
Python SDK
Seamlessly integrate Aiden into your existing Python projects with our lightweight package.
Auto-generated, Deployable Code
Get production-ready code based on your schema and transformation description.
Private & Secure
Your data never leaves your environment. Aiden builds and runs code locally or in your secure environments.

Transform your data with aiden

Aiden is your AI-powered data transformations.

Get Started Clone on GitHub