Label · data annotation company for AI
Data annotation
Human data annotation for text, image, video, audio and documents with taxonomy design, reviewer calibration, multi-stage QA and measurable acceptance criteria.
Designed for
ML teams that need consistent labels, defensible QA and domain-specific review rather than an unmanaged labor marketplace.
TrainLayer does not treat data annotation as a generic queue of tasks. The workflow is designed around the model objective, data rights, edge cases and the evidence required to accept delivery.
Scope this service ↗What you receive
Outputs tied to model performance.
Calibrated reviewers before production scaling
Documented error taxonomy and escalation rules
Auditable samples and acceptance reporting
Delivery process
A controlled path from scope to export.
01
Translate the model objective into a labeling taxonomy
02
Create gold examples and reviewer guidance
03
Calibrate reviewers and measure disagreement
04
Run production with targeted audits and rework loops
Capabilities
Built around the specification.
Exact workflows vary by modality and risk, but every engagement defines acceptance criteria before production scales.
Classification and tagging01
Bounding boxes and segmentation02
Transcription and OCR correction03
Multi-stage quality review04
Where this applies
Related AI industries.
Computer vision
Image and video datasets for detection, segmentation, tracking, OCR, quality inspection and safety systems.
View industry solution →Enterprise documents
Invoices, forms, receipts, handwriting and document-understanding datasets for extraction and workflow automation.
View industry solution →Generative AI
Instruction tuning, preference data, red-teaming, benchmarking and expert evaluation for language and multimodal models.
View industry solution →Frequently asked questions
What buyers usually ask.
Which annotation types do you support?+
Projects may include classification, tagging, bounding boxes, polygons, segmentation, tracking, transcription, OCR correction, ranking and rubric-based evaluation.
How do you measure annotation quality?+
Depending on the task, we use audited acceptance, inter-annotator agreement, gold-set performance, error categories and confidence-based sampling.
Can domain experts review the data?+
Yes. Expert review can be introduced where generalist labeling is not sufficient, including technical, enterprise and specialized domain tasks.
Start with the requirement
Scope a data annotation program.
Share the use case, modality, volume and target outcome. We will reply with the questions needed to define a credible pilot.
Talk to TrainLayer ↗