Data Annotation
Data annotation services
Label your own data so a model can learn from it or be tested against it, with written guidelines and spot-check quality.
The problem
You have collected hundreds of customer messages, product photos, or support tickets and you want a model to learn from them. The problem is that raw data is not training data. Someone has to read each example and mark it — this message is a complaint, that photo shows a defect, this ticket was resolved correctly, that one was not. And if the people doing the marking are applying the rules differently in their heads, the model learns from contradictions and behaves unpredictably. Good annotation is slower than it looks and more rigorous than most teams expect when they start.
Your model trains on examples you can stand behind.
Built for: Teams building or evaluating a model on their own domain-specific data.
What we deliver
Written annotation guidelines. Before a single label is applied, we write the rules down. Edge cases, ambiguous examples, what to do when something could go either way. Every annotator works from the same document, and the document is yours to keep.
Spot-check quality control. A sample of every annotator's output is reviewed against the guidelines. Disagreements are resolved against the written rules, not personal judgment. The error rate is measured and reported, not assumed to be acceptable.
Small and accurate over large and sloppy. A few hundred well-labelled examples train and evaluate a model more reliably than tens of thousands of rushed ones. We scope the dataset to what your model genuinely needs, not to fill a number.
A test set you can trust. We always separate a held-out evaluation set before annotation begins, so you can measure model performance against examples it has never seen and that were labelled correctly.
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Frequently asked questions
- Can you annotate data that contains our customers' personal information?
- Data handling and anonymisation are part of the scoping conversation. Depending on the sensitivity, we may work inside your environment rather than pulling data out, or redact identifiers before annotation begins. We do not handle personally identifiable data casually.
- How many examples do we actually need?
- It depends entirely on the task, the model, and how many categories you are labelling. We will not give you a number before seeing the data and the task. What we can tell you is that more examples do not compensate for inconsistent labels — quality comes first.
- We already have some labels that a team member applied informally. Can we use those?
- Sometimes yes, sometimes no. We audit existing labels against a written guideline before including them. Labels applied without a shared guideline often have a hidden inconsistency rate that degrades training. It is faster to find out early than after a model is built on top of them.