Annotation types
Multimodal Annotation
Cross-modal annotation where multiple data types must align.
- Image + text grounding
- Video + audio synchronization
- Sensor + visual data alignment
- Multimodal prompt-response datasets
Image Annotation
Pixel-accurate and object-level annotations for computer vision models across structured and unstructured imagery.
- Bounding Boxes
- Polygons
- Semantic Segmentation
- Keypoints And Landmarks
- Lines, Splines And Contours
Video Annotation
Frame-level and temporal annotations for motion-aware and sequence-based models.
- Object tracking across frames
- Action and activity recognition
- Event detection with timestamps
- Frame-by-frame segmentation
Text Annotation
Structured labeling for training and evaluating NLP and language understanding models.
- Named entity recognition
- Intent & slot labeling
- Relation extraction
- Document-level classification
LLM Data Annotation & Evaluation
Human-in-the-loop data creation and evaluation for large language models and generative systems.
- Prompt–response labeling
- Preference ranking
- Instruction tuning datasets
- Safety, bias, and policy evaluation
Audio & Speech Annotation
Speech and audio labeling for ASR, TTS, and audio intelligence systems.
- Transcription (verbatim, clean, phonetic)
- Speaker diarization
- Intent and emotion tagging
- Noise and acoustic event labeling
LiDAR & Point Cloud Annotation
3D annotation for spatial understanding in autonomous and robotics systems.
- 3D bounding boxes
- Point-wise segmentation
- Object classification
- Sensor fusion (camera + LiDAR)
Time-Series Annotation
Labeling of sequential and sensor-based data for forecasting, anomaly detection, and monitoring models.
- Event tagging
- Anomaly labeling
- Change-point detection
- Window-based classification
Multimodal Annotation
Cross-modal annotation where multiple data types must align.
- Image + text grounding
- Video + audio synchronization
- Sensor + visual data alignment
- Multimodal prompt-response datasets
Image Annotation
Pixel-accurate and object-level annotations for computer vision models across structured and unstructured imagery.
- Bounding Boxes
- Polygons
- Semantic Segmentation
- Keypoints And Landmarks
- Lines, Splines And Contours
Image Annotation
Pixel-accurate and object-level annotations for computer vision models across structured and unstructured imagery.
- Bounding Boxes
- Polygons
- Semantic Segmentation
- Keypoints And Landmarks
- Lines, Splines And Contours
Video Annotation
Frame-level and temporal annotations for motion-aware and sequence-based models.
- Object tracking across frames
- Action and activity recognition
- Event detection with timestamps
- Frame-by-frame segmentation
Text Annotation
Structured labeling for training and evaluating NLP and language understanding models.
- Named entity recognition
- Intent & slot labeling
- Relation extraction
- Document-level classification
LLM Data Annotation & Evaluation
Human-in-the-loop data creation and evaluation for large language models and generative systems.
- Prompt–response labeling
- Preference ranking
- Instruction tuning datasets
- Safety, bias, and policy evaluation
Audio & Speech Annotation
Speech and audio labeling for ASR, TTS, and audio intelligence systems.
- Transcription (verbatim, clean, phonetic)
- Speaker diarization
- Intent and emotion tagging
- Noise and acoustic event labeling
LiDAR & Point Cloud Annotation
3D annotation for spatial understanding in autonomous and robotics systems.
- 3D bounding boxes
- Point-wise segmentation
- Object classification
- Sensor fusion (camera + LiDAR)
Time-Series Annotation
Labeling of sequential and sensor-based data for forecasting, anomaly detection, and monitoring models.
- Event tagging
- Anomaly labeling
- Change-point detection
- Window-based classification
Multimodal Annotation
Cross-modal annotation where multiple data types must align.
- Image + text grounding
- Video + audio synchronization
- Sensor + visual data alignment
- Multimodal prompt-response datasets
Our Advantages
Understand how our data collection approach improves model quality, compliance, and time-to-market.
Optimized for quality
We have a two-layer QC process that ensures the quality of the output. This is enabled by a short feedback loop process.
End to end solutions
From data collection and cleaning to data annotation, we offer End to end solutions for your training data needs.
Cost efficient
Our pricing is transparent and economical. We are more cost-effective than contract workers and large annotation platforms.
Completely managed
Our services are fully managed with dedicated account managers to ensure smooth operations.
Scalable workforce
Start with a single person and grow with us. We scale our team based on your demands.
Data security
Data security is paramount. We are GDPR compliant and ISO 27001 certified.
Industries we serve
Purpose built AI data services for the workflows, challenges, and scale unique to your industry.
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