AI Data & Model Intelligence
Data Annotation
We run annotation workflows with trained contributors, clear guidelines and measurable review stages, so labeled data is consistent enough to train on and documented well enough to audit.
Challenges
What we help solve
- Ambiguous edge cases handled inconsistently
- Guideline drift over long programs
- High rework rates from low-quality vendors
- Specialist domains that require subject-matter expertise
Capabilities
Accurate labeling for text, image, audio, speech and video.
Text annotation
Classification, entity extraction, sentiment, intent and relation labeling.
Image and video
Bounding boxes, polygons, segmentation, keypoints and object tracking.
Audio and speech
Transcription, speaker diarization, timestamping and audio event tagging.
Guideline management
Living guidelines with versioned changes and edge-case libraries.
Delivery approach
How the work runs
- 1Discover
- 2Define
- 3Design
- 4Build
- 5Validate
- 6Deliver
- 7Improve
Deliverables
What you receive
- Labeled datasets
- Guideline versions and edge-case library
- Quality and agreement reports