Solution
Training data, collected by people you trust.
Structured forms for text, images, audio, and labels. Scale to thousands of contributors without losing quality.
Why dataset collection goes wrong
Contributors interpret loose instructions differently, so samples drift in quality.
Bad samples — wrong labels, missing media, off-spec formats — slip into the training set unnoticed.
Scaling from dozens to thousands of contributors multiplies the inconsistency.
Cleaning and reformatting exports to match your schema eats time before any training starts.
Dataset-grade collection
Describe it. Quola builds it.
Tell Quola what your dataset needs and AI scaffolds the form, validation, and skip logic.
Multi-modal by default
Text, images, audio, video, and structured labels in a single submission.
Quality at the source
Required attachments, validation rules, and reviewer queues stop bad samples reaching your dataset.
Export to your training pipeline
JSON, CSV, Parquet — keyed exactly to your schema. Plug straight into your trainer.
From spec to dataset
- 1
Describe the dataset
Tell Quola what samples you need; it scaffolds the form, labels, and validation.
- 2
Onboard contributors
Invite contributors across web and mobile — each sees only their task.
- 3
Collect with guardrails
Validation and required attachments keep every submission on-spec.
- 4
Review and export
Reviewers approve samples, then export keyed to your training schema.