AI Training & Data
Data Annotation
Accurate, consistent labels across image, video and text. Quality-checked annotation for computer vision, language and preference-learning datasets.
Overview
Why it matters, and how we handle it
Good annotation is mostly process: clear guidelines, trained annotators, independent review and fast feedback loops. That is exactly how we run every project.
Whether you need thousands of images boxed, hours of video segmented or language data ranked for RLHF, each item is labeled by a trained specialist, reviewed by a second, and verified at a final QA gate.
Where clients use it
- Computer-vision model training
- LLM fine-tuning and evaluation
- Content moderation and trust & safety
- Search, e-commerce and catalog quality
Capabilities
Exactly what we can do for you
Computer vision
- Bounding boxes, polygons, keypoints and landmarks
- Semantic and instance segmentation
- Video object tracking and event labeling
- Image classification and attribute tagging
Language and preference data
- Named-entity, intent and sentiment tagging
- Text classification and content moderation labeling
- RLHF preference ranking and response evaluation
- Prompt and response quality review
How we deliver
A managed process, not a hand-off
Every project follows the same five stages, so you always know where your work stands and who is responsible for it.
Brief and scope
We confirm your goals, volume, deadline and acceptance criteria, and agree a pilot batch where it makes sense.
Specialist match
Your project goes to team members approved for this category of work.
Production
Work is completed to your guidelines, with deadlines tracked and reminders built in.
Peer review
A second specialist checks accuracy and completeness and sends anything that falls short back for revision.
QA and delivery
A final quality gate signs off the deliverables before they reach you. Your feedback loops straight back to the team.
Built-in quality
Checked twice before it reaches you
Quality is part of the workflow. Nothing is delivered until it has passed both a peer review and a final QA check.
- Written guidelines and a calibration batch before scale-up
- Independent peer review of annotated work
- Final QA gate before delivery
- Performance ratings keep annotators accountable for quality
- Work is assigned only to named, vetted team members, and we follow the data-handling requirements you set out at scoping.
Questions
Good to know before you start
Do you annotate 3D and LiDAR data too?
Yes. See our dedicated LiDAR Annotation service for point-cloud labeling, tracking and sensor-fusion work.
How do you maintain quality at volume?
Every item passes peer review and a QA gate. Reviewer feedback goes back to the annotator, and ratings feed future task assignment.
Can you work in our annotation tool?
Tell us about your tooling at scoping. We will confirm whether we work inside your platform or deliver in your required format.
Explore more
Other services
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Clear, original, audience-focused writing: articles, marketing copy, research and newsletters.
Explore serviceReady to talk about data annotation?
Send us your requirements or a sample of your data or brief. We will come back with a scoped quote and a proposed pilot.