Choose the right data path
Every AI model depends on data, and there are various ways to get it. You can improve the data you already have or collect new data. Chemin supports both through data annotation and data collection.
Data Annotation
Label existing text, image, video, audio, and sensor data with task-specific guidelines and structured quality review.
Choose the right data path
Every AI model depends on data, and there are various ways to get it. You can improve the data you already have or collect new data. Chemin supports both through data annotation and data collection.
Data Annotation
Label existing text, image, video, audio, and sensor data with task-specific guidelines and structured quality review.
Data Collection
Plan and run new capture programs around the required data types, contributors, locations, and coverage.
Overcome delayed data development
Existing data or new collection
Review the data you hold against the coverage the project requires.
Keeping quality consistent
Shared criteria and review help reduce variation across contributors, sources, and project stages.
Planning coverage and rights
Collection requirements, consent, and data rights need to be addressed in the project plan before capture begins.
Data generation from source to success
End-to-end data services from sourcing and annotation to data generation for model training
100M+
30,000+
18
Data generation from source to success
End-to-end data services from sourcing and annotation to data generation for model training
100M+
30,000+
18
Why Chemin
Our team includes 50+ specialists across LLM and data engineering, has processed over 100 million data points, and operates across 18 countries. Chemin can support collection followed by annotation, so new data is held against the same project requirements and quality criteria.
Why Chemin
Our team includes 50+ specialists across LLM and data engineering, has processed over 100 million data points, and operates across 18 countries. Chemin can support collection followed by annotation, so new data is held against the same project requirements and quality criteria.
Frequently asked questions
Find answers to common questions about our services, process, and how we can support your needs.
When should I use Data Annotation or Data Collection?
Use Data Annotation when you have data that needs labels. Use Data Collection when the data or coverage required for the project does not yet exist.
Can collection and annotation be combined?
Yes. A project can collect new data and then apply task-specific annotation guidelines and quality checks to prepare it for downstream use.
How is annotation quality tracked?
Quality checks are scoped to the project. They can include review against gold-standard examples and annotator agreement tracking, with findings reported during delivery.
Can Chemin work with our existing data or guidelines?
Yes. Annotation can be scoped around your existing dataset, guidelines, taxonomies, and delivery requirements.
How are consent and data rights handled for collection?
Consent requirements and data rights are addressed in the collection protocol before capture begins, with details scoped to the project and locations.
Get a sample dataset for your project
Discuss the right data path and quality requirements with our team.