Services
The data your AI needs.
From human-perspective video and annotated audio to structured visual and language datasets, Mvaak helps AI teams build the data behind intelligent systems.
Egocentric Video Data
Human-perspective video data for AI systems learning to understand activities, interactions and real-world environments.
What we can do
- First-person task capture
- Activity and sub-action segmentation
- Hand and object interaction labelling
- Environment and context metadata
- Multi-participant collection
- Structured episode delivery
Feeds
Embodied AI · Robotics · Human Activity Recognition · Spatial Intelligence · Computer Vision · Multimodal AI · Human-Object Interaction · Autonomous Systems
Audio Annotated Data
Structured audio datasets for speech, voice and audio intelligence.
What we can do
- Transcription
- Timestamped segmentation
- Speaker labelling and diarisation
- Audio event classification
- Multilingual handling
- Quality and clarity review
Feeds
Speech Recognition · Voice AI · Conversational AI · Speaker Recognition · Audio Classification · Multilingual Speech AI · Audio Intelligence
Video Annotated Data
Structured visual datasets for computer vision and video intelligence.
What we can do
- Bounding boxes
- Object tracking across frames
- Frame and clip classification
- Activity and event labelling
- Temporal segmentation
- Consistency review across sequences
Feeds
Computer Vision · Object Detection · Object Tracking · Activity Recognition · Human-Object Interaction · Video Intelligence · Autonomous Systems
Text Annotated Data
Structured language datasets for NLP, LLM and conversational AI.
What we can do
- Classification and tagging
- Named entity annotation
- Intent labelling
- Sentiment annotation
- Span and relation marking
- Human validation of edge cases
Feeds
NLP · LLM Training · Text Classification · Entity Recognition · Intent Classification · Sentiment Analysis · Conversational AI · Language Intelligence
One operating standard
The same six gates, whichever data type you buy.
A corpus assembled across more than one category has to stay internally consistent, so the quality process does not change per service.
- 01
Collection
Capture or sourcing against a defined specification, so the input is already in scope.
- 02
Guidelines
A written definition of correct, with worked examples and explicit edge-case handling.
- 03
Annotation
Trained annotators working to that guideline, escalating ambiguity rather than guessing.
- 04
QA
Structural and consistency checks across the batch, not just within single items.
- 05
Human Validation
Review by a second person against the acceptance definition.
- 06
Final Dataset
Structured delivery with the guideline and review notes attached.
Need a custom dataset?
If your requirement does not map cleanly onto one of the four, it is usually a combination — describe the model and we will tell you what the data would have to look like.
Have a data challenge?
Let’s build the dataset behind it.
Tell us what you’re building, what data you need and the scale of your project.