Image and text pairs
Create captions, visual question-answer pairs and grounding annotations tied to source images.
Build multimodal AI
Different signals. One coherent dataset.
Build aligned text, image, audio, video and document datasets with annotation, provenance and evaluation designed around cross-modal behavior.
RECORD IDscene_0042
Source assets stay connected.
TextPrompt + reference answer
ImageRegion + grounding label
AudioTranscript + speaker
VideoClip + event timestamps
DocumentPage + structured fields
alignmenttimestamps · regions · referencesreviewannotations · provenance · QA
Failure-ledTarget weak behaviors
Expert-routedMatch the right judgment
QA-readyDeliver with evidence
Built around the real work
Capabilities that move a model from promising to useful.
Choose the parts of the data and quality workflow that match the behavior, market or modality you are building for.
BUILD
Designed for the next decision, not the last task.
Every capability is shaped around downstream use: what the model needs to learn, what reviewers need to judge and what your team needs to ship with confidence.
Talk through your workflow01
Video and temporal context
Connect clips, events and descriptions with timestamps and segment-level annotations.
Audio and transcript alignment
Keep speech, speakers, text and timing linked for cross-modal training and review.
Document understanding
Connect page images, extracted text, layouts and structured fields in a consistent record.
Manifests and metadata
Deliver asset references, annotation schemas and provenance fields that keep records traceable.
Cross-modal evaluation
Review whether responses are supported by the supplied image, audio, video or document evidence.
RECORD IDscene_0042
Source assets stay connected.
TextPrompt + reference answer
ImageRegion + grounding label
AudioTranscript + speaker
VideoClip + event timestamps
DocumentPage + structured fields
alignmenttimestamps · regions · referencesreviewannotations · provenance · QA
Alignment is part of the data
Every annotation points back to its source.
Use stable record identifiers and asset manifests to preserve the relationship between media, annotations and evaluation targets.
- Linked asset IDs and source references
- Temporal and spatial alignment where required
- Review notes, provenance and known limitations
The operating model
Build the connections before scaling the collection.
Pilot the record structure across representative modalities before expanding the program.
Define
Choose the task, modalities and alignment requirements.
Assemble
Collect or curate linked assets with provenance.
Annotate
Add captions, labels, timing and grounded references.
Validate
Check alignment, completeness and reviewer agreement.
Deliver
Package manifests, annotations and evaluation splits.
Quality that holds up in production
Review the relationship, not just the files.
Individually valid assets can still form an incorrect training example. Review correspondence and evidence across the complete record.
- Consistent asset identifiers and schema checks
- Cross-modal grounding and alignment review
- Documented data rights and task-specific limitations
AlignmentText, regions and timestamps reference the intended source
CompletenessRequired modalities and annotations are present
GroundingAnswers and labels are supported by source evidence
DeliveryManifests, splits and provenance remain connected
What arrives at the end
Deliverables your team can use.
Clear outputs, stable structure and the context needed to move the work into training, evaluation or production.
01Linked multimodal asset manifests
02Image captions and visual QA pairs
03Video and audio segment annotations
04Document layout and field labels
05Cross-modal evaluation sets
06Provenance, QA notes and limitations
Where it fits
Visual assistants
Teach models to answer questions grounded in images.
Document AI
Connect layout, text and fields for extraction tasks.
Audio-video understanding
Preserve speech, event and temporal context across recordings.
Start with the outcome
Connect your next multimodal data program.
Share the modalities, task and output schema. We’ll help define collection, alignment, annotation and review.