Annotation, specific to your model — not generic.
Bounding boxes, transcription, sentiment, preference ranking — labeled by trained annotators against your guidelines, with calibration and review built into every batch.
Computer vision, language, audio, and generative AI.
Only capabilities Ayta can currently deliver or is genuinely prepared to pilot are listed below.
Computer Vision
Image & VideoBounding boxes, segmentation, keypoints, tracking, and classification — across still images and video frames.
Bounding boxes
Objects marked with rectangular boxes for detection and localization tasks.
Polygon segmentation
Irregular object boundaries traced with polygon outlines for precise shape data.
Semantic segmentation
Every pixel in an image classified by category, not just the objects boxed.
Instance segmentation
Individual object instances separated and labeled, even when the same class overlaps itself.
Keypoints
Specific points on an object or body marked for structure and orientation data.
Pose estimation
Body or object position and orientation labeled across a set of reference points.
Object tracking
Objects labeled and followed consistently across frames as they move through a scene.
Image classification
Whole images tagged against your category set for training and filtering pipelines.
Video annotation
Video labeled frame-by-frame or across a clip's duration, depending on what your model needs.
Activity recognition
Actions and activities labeled across a clip's duration, not just a single frame.
NLP
Language · TXTClassification, extraction, and relevance labeling for language and retrieval models.
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.
Audio
Speech · AUDTranscription, speaker identification, and classification across languages and accents.
Transcription
Speech converted to accurate text, including disfluencies and non-speech sounds where your spec calls for it.
Speaker identification
Individual speakers labeled and distinguished across multi-speaker recordings.
Speech classification
Audio segments tagged against your category set for training and filtering pipelines.
Intent
The purpose behind an utterance labeled for voice assistant and dialogue models.
Emotion
Emotional tone labeled in speech where your model needs to detect affect.
Accent / dialect
Regional accent and dialect labeled to support coverage and fairness testing.
Audio segmentation
Recordings split into labeled segments — speech, silence, overlap, or background noise.
Generative AI
RLHF · EvaluationHuman preference ranking and structured evaluation for teams fine-tuning and assessing generative models.
Preference ranking
Annotators compare pairs or sets of model responses and rank them against your defined criteria.
Response evaluation
Full model responses reviewed end-to-end against your guidelines, not scored from isolated fragments.
SFT
Demonstration data and reference responses produced for supervised fine-tuning.
RLHF
Human preference data structured for reinforcement learning from human feedback pipelines.
DPO preference data
Chosen/rejected response pairs produced for direct preference optimization fine-tuning.
Safety evaluation
Responses checked for harmful, unsafe, or policy-violating content before anything is reported back.
Factuality
Claims in model output checked against ground truth or source material you provide.
Helpfulness
Responses judged on whether they actually solve the user's request, not just whether they're well-formed.
Instruction following
Responses checked against the specific instructions in the prompt, not just general quality.
Have a data requirement?
Give us the specification — we'll build the operation around it.
Start a Pilot →