Services/AI language data

SERVICE 04 / AI LANGUAGE DATA

AI LANGUAGE
DATA.

Give multilingual AI a stronger human foundation. Create and evaluate language data with native-language judgment and a clear task definition.

THE WORK,
IN MORE DETAIL.

Scope and delivery requirements are agreed for each project. Use these tracks as a starting point.

01 / AI LANGUAGE DATA

Data creation

Develop prompts, text samples, and conversations that fit the language, domain, and schema.

02 / AI LANGUAGE DATA

Annotation & labeling

Apply task-specific labels using shared guidelines and worked examples.

03 / AI LANGUAGE DATA

Quality review & adjudication

Resolve inconsistent labels and document decisions against an agreed rubric.

04 / AI LANGUAGE DATA

Model evaluation

Review outputs for the qualities your task requires, including clarity, accuracy, and tone.

05 / AI LANGUAGE DATA

Sensitive-data review

Identify information covered by the agreed privacy taxonomy and flag ambiguous cases for review.

06 / AI LANGUAGE DATA

Multimedia language support

Support text in image and video datasets through caption work, OCR correction, and descriptions.

START WITH THE BRIEF

Context makes
the difference.

Describe the model task, languages, domain, sample data, annotation guidelines, volume, and evaluation criteria.

Discuss ai language data ↗

PLAN THE HANDOVER

Agree the output.
Then build toward it.

Set the format, review criteria, and delivery process before work begins.

JSONJSONLCSVYour agreed schema

Make the task clear before creating the data.

Multilingual AI work needs more than a list of languages. Define the model task, the domain and the decisions contributors should make so data creation and evaluation follow the same brief.

01 / PROJECT PLANNING

Define inputs and outputs

Share representative samples and the expected schema. Describe whether contributors will create text, label existing material, review model outputs or resolve disputed annotations.

02 / PROJECT PLANNING

Write usable task guidelines

Provide label definitions, worked examples and a rubric for judgments such as clarity, accuracy or tone. Identify ambiguous cases and explain how they should be escalated for review.

03 / PROJECT PLANNING

Agree quality review

Define how inconsistent labels will be reviewed and decisions documented. Confirm the delivery format, required fields and any sensitive-data categories covered by the task.

Examples of project briefs

Use these starting points to describe the work you have in mind.

Multilingual annotation: provide a label set and worked examples so contributors can apply the same definitions across the requested languages.
Model evaluation: share prompts, outputs and a scoring rubric that explains what successful responses should contain and which errors reviewers should flag.

AI language data questions

What kinds of language-data tasks can I discuss?

The published service scope includes text and prompt creation, annotation, quality review, adjudication and model evaluation. It also covers agreed sensitive-data review and language work such as captions, OCR correction and descriptions.

How are ambiguous annotations handled?

Agree an escalation and adjudication process in the brief. Worked examples and documented decisions help clarify how the rubric applies when contributors encounter an uncertain case.

What delivery formats can be discussed?

JSON, JSONL and CSV are starting points. Share your schema, field definitions and validation requirements so the output can be planned around your workflow.

LET’S DEFINE YOUR PROJECT.