AI-Ready Data:
Trustworthy AI starts with trustworthy data

Ensure AI models only use well-understood, governed data, so that AI initiatives deliver value without exploding risk.

4.7/5 stars
100+ reviews
Dataedo Data Catalog Interface
Join 1000+ Satisfied Customers Using Dataedo
Vanderbilt University
Bridgestone
Garmin
Roche
Federal Reserve Board
Fujitec
Hunter Engineering
KPMG
ArcelorMittal
National Film Board of Canada
NHS
SportClips
IOWA
M&T Bank
Dublin City Council
PBS

Govern model inputs with catalog, lineage, and PII classifications

AI models only perform reliably when they consume governed, well-understood data. Dataedo catalogs upstream datasets, traces feature engineering pipelines with lineage, and classifies sensitive inputs, so your AI team builds models that regulators, auditors, and stakeholders can trust.

The challenge

Undocumented data feeding models

Undocumented data feeding models

AI models rely on features sourced from tables that lack proper documentation, ownership, and data quality visibility. This increases operational and regulatory risk.

No visibility into sensitive inputs

No visibility into sensitive inputs

There is no clear view of which PII, financial data, or protected attributes flow into training pipelines, making risk exposure difficult to assess.

Pipeline drift & model instability

Pipeline drift & model instability

Feature engineering workflows evolve quickly, creating undocumented changes that introduce drift and quietly degrade model performance.

No proof for auditors

No proof for auditors

When regulators or internal auditors ask for evidence that prohibited data sources aren’t used, teams struggle to provide clear, traceable documentation.

How Dataedo helps

Catalog all AI upstream data

Connect Dataedo to data warehouses, feature stores, and batch/stream sources. Organize training datasets under subject areas like Customer Risk or Fraud Detection.

Catalog all AI upstream data

Document features in business language

Use the Data Catalog and Business Glossary to define model inputs in clear, business-friendly terms. Capture definitions, calculation logic, and quality notes so both technical and non-technical stakeholders understand exactly what each dataset represents and how it’s used in AI models.

Document features in business language

Build end-to-end AI pipeline lineage

Use Data Lineage to trace raw source tables through preprocessing, feature engineering, and model retraining workflows. Create a clear, visual map of how data flows into AI models to support transparency, impact analysis, and audit readiness.

Build end-to-end AI pipeline lineage

Classify and govern sensitive model inputs

Apply classification tags to mark PII, financial data, or protected attributes in training features. Document masking rules and exclusion decisions.

Classify and govern sensitive model inputs

What you get

Compliant AI models

Compliant AI models

Provide auditors with a full view of your AI ecosystem by showing exactly which data feeds and inputs each model uses, ensuring complete transparency and traceability.

Reliable model performance

Reliable model performance

Avoid unexpected outcomes by monitoring upstream data changes and guaranteeing that every model performs consistently.

Faster AI governance reviews

Faster AI governance reviews

Accelerate your compliance workflows by generating detailed, up-to-date documentation directly from your live model catalog, reducing manual effort and review cycles.

Key features
Business Glossary

Business Glossary

Define and document model features in clear business language, including definitions, calculation rules, and quality notes, so your team and auditors understand exactly what each input represents.

Data Lineage

Data Lineage

Trace every feature from raw source tables through feature engineering and model training workflows, giving you a complete, end-to-end view of how data flows into your AI models.

Sensitive Data Discovery

Sensitive Data Classification

Identify and label PII, financial data, or protected attributes in model inputs, including masking rules and exclusion decisions, to ensure your AI models comply with privacy and regulatory requirements.

FAQs

What is AI-ready data and why is it important?
AI-ready data is well-governed, documented, and high-quality data that can be reliably used to train AI models. It ensures consistent model performance, reduces bias, and helps teams meet regulatory requirements.
How does Dataedo improve AI model reliability?
AI models are only as good as the data they consume. Dataedo provides a unified data catalog that documents the source, quality, and definition of every feature used in your models. By understanding the "why" and "how" behind your data, data scientists can avoid using corrupted or irrelevant datasets, significantly reducing model drift and unexpected failures.
What is AI data lineage and why is it important?
AI data lineage is the visual map of how data moves from raw source systems through feature engineering and into your training sets.
It is critical for compliance and model debugging because it provides a transparent audit trail. If a model produces biased or inaccurate results, lineage allows your team to trace back to the exact transformation or source table where the data was altered, saving weeks of manual troubleshooting.
Can you manage "Model Drift" using data lineage?
Model drift often happens because of undocumented changes in upstream data (e.g., a column name changes or a sensor starts sending null values). By using End-to-End Lineage, your data teams get an early warning system. You can trace a failing model back to its data dependencies to see exactly which pipeline change broke the performance, reducing "Mean Time to Repair" (MTTR) for AI incidents.
Why is metadata management critical for AI Agents?
AI agents and LLMs rely on RAG (Retrieval-Augmented Generation) to provide accurate answers. If your metadata is messy, the agent will retrieve the wrong context. Dataedo ensures that your "business glossary" is synchronized with your data catalog, providing the semantic layer that AI agents need to understand the difference between "Gross Revenue" and "Net Profit" before they answer a stakeholder's question.
Why our customers love Dataedo?
Piotr Kononow

Piotr Kononow

Founder

Trustworthy AI starts with trustworthy data

Explore Dataedo through a preconfigured data catalog with sample data, try it with your own data during a 14-day free trial, or book a demo.