Markup

Markup helps you turn free-form text into structured data for NLP and ML—without the hassle of building everything manually. It’s designed to rapidly generate annotation-ready datasets so you can spend less time on repetitive labeling and more time on modeling.

With AI assistance, Markup speeds up the annotation workflow by suggesting annotations based on the text you’re working with. This context-aware support can help reduce manual effort while keeping your outputs more consistent as your dataset grows.

Markup is useful for teams and individuals creating annotation-heavy datasets such as named-entity recognition, concept mapping, sentiment analysis, and other structured labeling tasks. It supports both standard and custom ontologies, so you can match the structure you need for your specific project.

To start, sign up for an account and try the demo to explore the workflow before committing. Support, documentation, and help resources are available to guide you as you build your structured datasets.

What does Markup do?

Markup turns free-form text into structured datasets for NLP and ML using AI-assisted annotation.

How does Markup help with annotation work?

It provides AI suggestions for annotations so you can move faster and spend less time on repetitive labeling.

What kinds of dataset tasks can I build with Markup?

You can create datasets for tasks like named-entity recognition, concept mapping, sentiment analysis, and other annotation-heavy workflows.

Does Markup support custom annotation structures?

Yes—Markup supports both standard and custom ontologies to fit the structure your project requires.

Can I try Markup before signing up?

Yes. You can try a demo to explore features and the annotation workflow before you start.

Where can I find help and documentation?

Markup provides documentation and support resources to help you get started and use the tool effectively.