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Data labeling

Good labels make the judgment behind a model or analysis clear. We turn your rules and examples into a guide that annotators can follow. We test the guide on a small set, discuss unclear cases with your team, and then label the agreed files. The handoff includes the labeled data, guide, and review notes.

Discuss your data need

What you receive

  • A labeling guide with definitions, examples, exclusions, and edge cases
  • A labeled dataset in an agreed structure with record IDs preserved
  • A review summary showing unclear or frequently disputed cases
  • Suggestions for the label set or the next round of work

What we need to begin

  • Representative source material and the task the labels will support
  • Your initial categories, policy rules, or examples of good and poor outcomes
  • Required output schema, languages, and any review priorities

How we work

  1. 01

    Define labels and write examples for common and difficult cases

  2. 02

    Test the guide on a small set and settle unclear cases with your team

  3. 03

    Label the files, review the results, and deliver the data with the guide

Assess the fit for your project

Data labeling classifies, extracts from, or judges raw text or other agreed records under an executable task specification, delivering reviewable results through sampling and QA. Good scope begins with whether the task fits the material and rules can be applied consistently.

What tasks are suitable for data labeling?

Tasks suit labeling when they have clear units, label definitions, and decidable evidence, such as classification, entity recognition, sentiment, or other agreed tasks. If a task depends on absent context, expert conclusions, or vague goals, narrow it or improve the specification first.

How can labeling rules be tested for executability?

Use representative samples to write label definitions, positive and negative examples, edge cases, and review rules, then inspect disagreement records. Exceptions requiring business or domain judgment should be explicitly escalated; labeling is not medical, legal, or investment advice.

What drives labeling-project pricing?

Pricing commonly depends on record volume, label count and hierarchy, context length, rule complexity, language, required domain review, sampling and acceptance standards, file format, and rework scope. A small specification pilot makes quotation more accurate.

FAQ

Frequently asked questions

Can the label set change during the project?

Yes. Early calibration is designed to reveal unclear categories. We document changes so the final dataset and guidance remain understandable.

Do you support custom schemas?

Yes. We can work from an existing schema or help define one around the task, as long as the labels can be explained with usable examples.

Deliverables are assessed against agreed specifications, sampling and acceptance criteria. New fields, rule changes or expanded scope require separate agreement. Domain annotation is not medical, legal or investment advice, or regulatory certification.Sources and usage ↗

Begin with a clear question.

Discuss your data need