Your Data.
Instantly Ready
for AI.
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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.
DTK
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.
DTC
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
From Raw Data to ML-Ready Dataset
A simple four-step workflow to prepare, process, and export AI-ready data.
Use Cases Built for Real-World Data Workflows
See how AutoData supports different machine learning and analytics scenarios.
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Use Cases Built for Real-World Data Workflows
See how AutoData supports different machine learning and analytics scenarios.
- Prepare customer behavior data for churn modeling
- Detect missing values and inconsistent records
- Export clean, model-ready datasets faster
- Structure transaction data for anomaly-focused models
- Improve data quality before risk scoring
- Standardize fields across large datasets
- Organize behavioral and demographic inputs
- Build cleaner datasets for clustering and profiling
- Reduce preprocessing time for analytics teams
- Clean campaign and conversion data
- Prepare structured datasets for attribution and forecasting
- Improve reporting consistency across sources
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.
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.
Flexible Pricing for Every Data Workflow
Explore AutoData’s scalable pricing model based on your processing needs and business requirements.