Turn Raw Data Into AI-Ready Data, Automatically.
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Transform Raw Data Into AI-Ready Datasets
AutoData combines key data preparation capabilities in a single platform designed to transform raw datasets into AI-ready data.
Automated Data Preparation
Prepare datasets without manual preprocessing.
Machine Learning
Ready Output
Get structured data ready for training.
Cloud-Based
Processing
Process datasets without infrastructure complexity.
Scalable
Workflows
Handle datasets of different sizes efficiently.
Choose a Pipeline. Get AI-Ready Data
AutoData provides preset pipelines designed for common machine learning workflows.
Select a pipeline and automatically perform multiple preparation steps.
DCV
Executes web searches, API requests, and LLM queries to contextually fill missing values, validates existing content against independently sourced answers, and overwrites errors or appends new columns based on preference.
DTC
Standardizes currencies to USD, unifies dates to MM/DD/YYYY, converts percentages to decimals, clears cells with NaN values, and offers 10+ other fixations for format-consistent data.
DTN
Scans all columns; tokenizes texts, audios, and images; encodes categories; and converts dates into meaningful time-based values ensuring the entire dataset is consistent and ready for machine learning.
MDH
Tests multiple imputation and removal strategies for handling gaps, runs quick model checks, and automatically selects the method with the lowest prediction error.
CDS
Automates scaling by applying several industry-standard techniques, evaluating performance, and selecting the most effective approach for balanced feature representation.
DSM
Identifies overlaps, removes repetitive columns, and detects underlying similarity patterns. The result is a leaner and more powerful dataset.
DSG
Learns the "digital DNA" of your optimized dataset and generates new synthetic points that are statistically indistinguishable from the original.
Preset Pipelines
AI Starts With Data.
Get Your Data Ready.
Artificial intelligence depends on high-quality, structured data.
But most raw datasets contain missing values, inconsistencies and unprocessed features that prevent them from being used directly in machine learning.
Before models can be built, data must be prepared.
Core Features Built for Smarter Data Preparation
Automated Data Cleaning
Automatically detect formatting issues, duplicates, and incomplete records to make raw datasets more consistent and reliable.
Feature Engineering
Generate model-ready features and improve dataset usability with automated transformations and preprocessing logic.
Dataset Profiling
Quickly understand the structure, quality, and dimension of your dataset before moving into training or analysis.
Anomaly Detection
Identify unusual values, outliers, and problematic records early to improve downstream model performance.
ML Pipeline Automation
Create preprocessing workflows that simplify data preparation and reduce manual effort across projects.
AI-Ready Export
Export clean, structured, and machine-learning ready datasets for use in training, analytics, and production workflows.
From Raw Data to ML-Ready Dataset
A simple four-step workflow to prepare, process, and export AI-ready data.
Connect to Any Data Source
Databases
Data Warehouses
Storage & Files
Streaming & IoT
SaaS & Apps
Case Study: Turning Raw HR Data into
AI-Ready Training Data
An HR dataset containing inconsistent formats, missing values and limited records was transformed into a structured dataset suitable for machine learning using AutoData’s automated preparation pipelines.
Fragmented and Incomplete HR Data - 1,000 Records with Quality Issues
The original dataset contained inconsistent formats, duplicate entries and missing values across several fields. These issues made the dataset unreliable for machine learning training and required significant preprocessing before it could be used.
Automated 7-Step Data Preparation
AutoData processed the dataset through its automated preparation pipeline, applying validation, data cleaning, numericalization, missing data handling, scaling, noise reduction and high-fidelity data generation. This automated workflow prepared the dataset without manual intervention.
Machine Learning–Ready Dataset - From 1,000 to 20,000 Records
After processing, the dataset was transformed into a clean, structured format ready for machine learning workflows. Synthetic data generation expanded the dataset from 1,000 to 20,000 records, improving its suitability for model training.
Get Your Data Ready
for AI
Stop spending time manually preparing datasets.
AutoData automatically transforms raw datasets into AI-ready data so teams can focus on building and improving machine learning models.
Start preparing your data today.