Data for multimodal AI
Paired and aligned data across text, speech, image and video, so models learn how the signals relate.
What Multimodal AI needs
Multimodal models need pairs and sets that really match: a caption that describes the picture, a transcript that fits the audio, a question that depends on the image.
We build aligned multimodal datasets, checked so that every part of an item agrees with the others.
Data types
- Text
- Speech
- Image
- Video
Industries
- Technology and software
- Automotive
- Retail and e-commerce
- Healthcare
- Media and entertainment
- AR and VR
The data it takes
Image and caption pairs
Human-written captions in several languages.
Audio, video and transcript
Time-aligned speech with the footage it belongs to.
Visual question answering
Questions that need the image to answer, with checked answers.
Document and structure
Page images paired with structured output.
Screen recordings
Interaction footage labelled with intent.
How we collect it
- 01
Designed as sets
Each item is planned as a set so the parts belong together.
- 02
Written and recorded by people
Captions, questions and speech come from contributors, not from a model.
- 03
Cross-checked
Reviewers confirm that each part agrees with the others.
- 04
Delivered aligned
One manifest links every file and label in an item.
Services that deliver it
Related datasets
Visual question answering
Image and question pairs with grounded answers and reasoning traces.
Learn more
Image and caption pairs
Descriptive captions written by native speakers, not machine translated.
Learn more
Document image to structured output
Document images paired with the structured JSON a model should produce.
Learn moreTell us about your Multimodal AI project.
Describe the model and what it gets wrong today. You get volumes, languages, a quality target and a price, usually inside two working days.
- Reply within one working day
- A pilot batch you accept before we scale
Questions we get asked
How do you check alignment?
A reviewer confirms each part of an item against the others, and mismatches are returned for rework.
Do you provide the manifest format we need?
We deliver a manifest in JSONL or your own schema linking all files in each item.
