What we annotate

Annotation across every major data modality

We support structured labeling for the modalities and use cases that power modern AI systems.

Image annotation

Bounding boxes, polygons, polylines, keypoints and segmentation masks for detection, classification and segmentation models.

Video annotation

Frame-level and temporal labeling for action recognition, tracking and video understanding pipelines.

Text & NLP

Entity recognition, intent classification, sentiment tagging and document annotation for language models and NLP systems.

Audio & speech

Transcription, speaker diarization and speech labeling for ASR, speech-to-text and conversational AI training.

LiDAR & 3D

Cuboid and point-cloud annotation for autonomous systems, robotics and spatial AI programs.

Healthcare data

Review-supported labeling for medical imaging and clinical text, handled under project-specific confidentiality terms.

Annotation formats & tasks

Choose the right structure for each modality.

Task definitions, output formats, reviewer coverage and acceptance criteria are agreed during project scoping.

2D image labeling

Image classification, bounding boxes, polygons, polylines, keypoints and landmarks.

Segmentation

Semantic and instance masks, object boundaries and project-specific segmentation labels.

3D & LiDAR

3D cuboids, point-cloud segmentation and spatial relationships.

Video & tracking

Frame-level annotation, object tracking, temporal events and action labels.

Documents & OCR

Text regions, document layout, field extraction and OCR verification.

Audio & language

Audio transcription, diarization, translation, text labeling and LLM human evaluation.

Human evaluation

Structured response quality, relevance, preference and instruction-following review.

Quality assurance

Guideline calibration, reviewer sampling, senior QA, validation and rework.

How annotation works

The annotation lifecycle

01
Requirements

Schema and guidelines

We define the label taxonomy, attribute set, edge-case rules and annotation guidelines with your team.

02
Calibration

Pilot batch

A pilot batch runs against your hardest samples first. Guidelines are sharpened from real disagreement.

03
Production

Annotate

Trained annotators apply labels at scale under documented guidelines and daily reviewer oversight.

04
QA

Review and validate

Multi-level QA — annotator, reviewer and senior QA — with sampling, consistency checks and rework loops.

Ready to label your data?

Share your annotation requirements to discuss scope, quality criteria and possible pilot options.

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