“Our labelers disagree with each other.”
The model learns the confusion.
Data Annotation and Labeling Services
Your model can only be as good as its labels. We label your data carefully and consistently, and prove the quality with test sets and agreement scores. Start with a small pilot and see the numbers first.
shoebagprice tag
- Text
- Images
- Video
- Audio
- Conversations
- Product data
Bad labels cost more than you think
“The guidelines don’t cover real cases.”
Everyone guesses, differently.
“Quality drops as the dataset grows.”
Nobody measures it until the model fails.
We solve this by testing the guidelines, training people before they start, and measuring quality in every batch.
Five checks behind every label
- 1
Guideline review
We test your guidelines on real data and send you a list of unclear cases.
- 2
Training and test
Annotators practise and must pass before touching live data.
- 3
Gold test items
Hidden items with known answers measure accuracy every day.
- 4
Double labeling
Some items are labeled twice so we can measure agreement.
- 5
Error analysis
We group mistakes by cause and fix the guideline or training.
From your guidelines to a finished dataset
The pilot is where we catch problems. By the time we scale, the rules are clear and everyone is trained.
Your data and guidelines
Pilot batch labeled
Quality measured
Hits the target?
Yes
Scale to full volume
No
Fix guideline and retrain
Delivered with a quality report
Annotation types
We only take on annotation types we have the people, tools and process for.
We work in your annotation platform or one you approve.
Text
- Classification
- Sentiment
- Intent
- Entity labeling
- Content tagging
Images & video
- Bounding boxes
- Tagging
- Categorization
- Content review
Audio
- Tagging
- Transcript checks
What our labeled data powers
AI model training
Clean, consistent examples for training and fine-tuning.
Trust and Safety models
Labeled harmful content, from a team that knows moderation.
Search and recommendations
Relevance labels that improve results.
Product categories
The right category and attributes for every item.
Language data
Intent and sentiment from real conversations.
Content classification
Topic and type tags for large content libraries.
How an annotation project runs
- Step 1
Write the guide with you
Definitions, edge cases and examples of correct and incorrect labels.
Annotation guide v1
- Step 2
Calibrate
Everyone labels the same set until agreement reaches the target.
Agreement report
- Step 3
Label in batches
Each batch is reviewed and scored before delivery.
Batch quality score
- Step 4
Improve the guide
Disagreements become new rules, and the guide is versioned.
Guide changes and error analysis
Timings depend on volume and complexity. We agree them with you in writing before the pilot starts.
How we keep labels consistent
Gold examplesKnown-correct items are mixed in to check each reviewer.
Second reviewA sample is re-labeled by someone else.
Agreement trackingWe report how often reviewers agree, by label type.
Data access limitsPeople see only the data assigned to them.
Who you deal with
ML engineer
Labels you can trust and a clear list of confusing cases.
Project lead
Runs the team, reports quality and handles questions.
Product owner
Delivery dates tied to measured accuracy.
Start a data annotation pilot
Send a sample of your real data and your guidelines. We label it, measure the quality and show you the results before you commit to more.
What you get
- Guideline review with a list of questions
- Trained, tested annotators
- Labeled pilot batch
- Accuracy and agreement scores
- Plan and quote for full volume
Straight answers
Still have a question? Ask us directly.
Do you provide an annotation tool?
We work in your platform, or one you approve. That way your data stays where you control it.
How do you measure quality?
With hidden gold test items, double labeling to measure agreement between annotators, and regular QA samples. You get the numbers in every report.
Can you do medical or self-driving car data?
No. Those need specialist experts we do not claim to have. We focus on text, images, video, audio and product data.
How big should the pilot be?
Big enough to test the guidelines on real variety. We suggest a size once we see your data.
What accuracy can we expect?
It depends on how clear the task is. We do not quote a number before we have seen your data. The pilot gives you a measured figure, and you decide if it is enough.
Ready to build better operations
Tell us what you need to run, fix, or growWe will map out the right team and a pilot plan