Text annotation.
Classification, extraction, and relevance labeling reviewed by linguists — not just spell-checked — so labels hold up against your model's actual training pipeline.
Language data, labeled for training and retrieval.
Only capabilities Ayta can currently deliver or is genuinely prepared to pilot are listed below.
Text classification
Documents or passages tagged against your category set for training and filtering pipelines.
Named entity recognition
People, places, organizations, and other entities identified and labeled within text.
Intent classification
The purpose behind a query or message labeled for dialogue and search models.
Sentiment
Positive, negative, or neutral tone labeled at the document or sentence level.
Search relevance
Query-result pairs judged for relevance to train and evaluate retrieval models.
Content classification
Text sorted into topical or policy categories against your defined taxonomy.
Prompt evaluation
Prompts and completions reviewed for quality, safety, or fit against your criteria.
Need source text collected first? See Data Collection.
From guidelines to a labeled dataset.
Every annotation program follows the same operating sequence.
Guideline intake
Your taxonomy, edge cases, and labeling rules are built into the task design before annotation starts.
Annotator training
Annotators are trained and qualification-tested against your specific guidelines, not a generic label set.
Labeling
Text is labeled to spec — classification, entities, sentiment, or relevance, depending on your task.
QA & calibration
Second-pass review, inter-annotator agreement checks, and dispute resolution run on every batch.
Delivery
Labeled data packaged and delivered in the format your training pipeline expects.
Have a data requirement?
Give us the specification — we'll build the operation around it.
Start a Pilot →