Data Labeling
AI Data Annotation & Labeling
Get high-quality, expertly labeled training datasets that make your AI models more accurate and reliable.
- domain experts
- multi-stage QC
- any modality
- scales on demand
- Expert
- Domain-aware labelers
- Annotators who understand your industry context, not a generic crowd. Vetted for the work.
- 0-stage
- Quality control
- Consensus, gold-standard checks, and expert review on every dataset, with measured agreement.
- Any
- Data modality
- Images, video, text, documents, and audio, plus RLHF preference data and model evaluations.
02 / What we label
Human data for every modality
Your model is only as good as its data. We produce labeled datasets across every modality, made by domain-aware experts and checked at every stage.
- ImagesVisionBounding boxes, segmentation masks, keypoints, and fine-grained classification.
- VideoVisionFrame-by-frame tracking, event tagging, and activity labels.
- TextLanguageClassification, named entities, sentiment, intent, and span labeling.
- DocumentsLanguageField extraction, layout, tables, and key-value pairs from real paperwork.
- AudioAudioTranscription, speaker diarization, and intent for speech models.
- Preference dataAlignmentPairwise rankings and ratings that power RLHF and DPO.
- EvaluationsAlignmentRubric scoring, red-teaming, and expert model grading.
03 / How quality is made
How a dataset is built
Raw data in, expert labeling with AI pre-labeling for speed, multi-stage quality control, and a clean, consistent dataset delivered to your pipeline.
- TriggerRaw dataUnlabeled images, text, or audio.
- AI stepPre-label + QAAI proposes labels; quality is checked at scale.
- IntegrationAnnotation pipelineLabeled data flows to your training set.
- OutputLabeled dataset deliveredClean, consistent ground truth, faster.
04 / What it changes
What the build is designed to do
- 01Dramatically improve AI model accuracy with high-quality labeled training data
- 02Access domain-expert annotators who understand your industry context
- 03Ensure dataset consistency through rigorous multi-stage quality control
- 04Accelerate AI model development by eliminating the data preparation bottleneck
- 05Scale annotation capacity up or down based on project needs
05 / Use cases
What teams have us label
A few of the datasets local teams commission first.
- 01A local retailer annotates thousands of product images with bounding boxes and category labels to train a custom visual search and inventory recognition model
- 02A regional restaurant group labels thousands of customer reviews with fine-grained sentiment tags, food quality, service speed, ambiance, value, to train a feedback analysis model that identifies specific improvement areas
- 03A local healthcare provider annotates medical imaging data with expert radiologist oversight to train a screening assistance model, with strict HIPAA-compliant handling throughout
- 04A law firm labels contract clauses by type, risk level, and negotiability to train a document analysis model that accelerates contract review
08 / FAQs
AI Data Annotation & Labeling questions
What types of data can you annotate?
We handle all major data types for AI training: image annotation (bounding boxes, segmentation masks, keypoints, classification), text annotation (sentiment, entity recognition, intent classification, summarization), document annotation (information extraction, table recognition, layout analysis), audio annotation (transcription, speaker diarization, emotion detection), and video annotation (object tracking, action recognition, temporal segmentation). If your data type is not listed here, reach out, we have likely worked with it or can develop an annotation workflow for it.
How do you ensure annotation quality and consistency?
Quality control is built into every step of our process. We start with detailed annotation guidelines co-developed with your team. Annotators are trained on your specific domain before beginning work. Every annotation is reviewed by a second annotator, and disagreements are resolved by a senior reviewer. We track inter-annotator agreement metrics continuously and retrain annotators when consistency drops. Random samples are audited by our quality team, and we provide transparent quality reports with every delivered dataset. Our target is 95%+ annotation accuracy on every project.
How much does data annotation cost?
Costs vary based on data type, annotation complexity, volume, and required expertise level. Simple image classification might cost $0.02-0.10 per image, while complex segmentation or medical annotation can range from $0.50-5.00 per image. Text annotation typically costs $0.05-0.50 per document depending on complexity. We provide detailed pricing after reviewing sample data and annotation requirements. For most local business AI projects, the total annotation cost is a small fraction of the overall model development budget and has an outsized impact on model quality.
Can you handle sensitive or confidential business data?
Yes. We implement strict data security protocols for all annotation projects. This includes NDA agreements with all annotators, secure annotation platforms with access controls and audit logging, encrypted data transfer and storage, and compliance with relevant regulations like HIPAA for healthcare data. For highly sensitive data, we offer dedicated annotation teams who work exclusively on your project in controlled environments. We can also set up on-premise annotation workflows where your data never leaves your systems.
Turn AI Data Annotation & Labeling into something your team actually uses.
Name the work you want this to handle. We will map the build, show what is worth doing first, and what it costs. If there is no fit, we will say so.