The most flexible data labeling platform to fine-tune LLMs, prepare training data, or evaluate AI models.
We help you organise, label, and leverage your data to train or adapt models for search, classification, recommendation, routing, and forecasting — across text, images, audio, and video.
Label every data type
GenAI
LLM Fine-Tuning: Label data for supervised fine-tuning or refine models using RLHF.
LLM Evaluations: Response moderation, grading, and side-by-side comparison.
RAG Evaluation: Use Ragas scores and human feedback.
Images
Image Classification: Put images into categories.
Object Detection: Detect objects on image, boxes, polygons, circular, and keypoints supported.
Semantic Segmentation: Partition image into multiple segments. Use ML models to pre-label and optimize the process.
Audio & Speech
Classification: Put audio into categories.
Speaker Diarization: Partition an input audio stream into homogeneous segments according to the speaker identity.
Emotion Recognition: Tag and identify emotion from the audio.
Audio Transcription: Write down verbal communication in text.
Text, Documents, Chatbots
Classification: Classify document into one or multiple categories. Use taxonomies of up to 10000 classes.
Named Entity: Extract and put relevant bits of information into pre-defined categories.
Question Answering: Answer questions based on context.
Sentiment Analysis: Determine whether a document is positive, negative or neutral.
Time Series
Classification: Put time series into categories.
Segmentation: Identify regions relevant to the activity type you're building your ML algorithm for.
Event Recognition: Label single events on plots of time series data.
Video
Classification: Put videos into categories.
Object Tracking: Label and track multiple objects frame-by-frame.
Assisted Labeling: Add keyframes and automatically interpolate bounding boxes between keyframes.
Key capabilities
Flexible and configurable
Configurable layouts and templates adapt to your dataset and workflow.
Integrate with your ML/AI pipeline
Webhooks, Python SDK and API allow you to authenticate, create projects, import tasks, manage model predictions, and more.
ML-assisted labeling
Save time by using predictions to assist your labeling process with ML backend integration.
Connect your cloud storage
Connect to cloud object storage and label data there directly with S3 and GCP.
Explore & understand your data
Prepare and manage your dataset in our Data Manager using advanced filters.
Multiple projects and users
Support multiple projects, use cases and data types in one platform.
From messy data to reliable models
Data audit & objective definition
We review what data you have, what you need to predict or automate, and define clear evaluation metrics before any training begins.
Label strategy & sampling
We design labelling guidelines, sampling strategies, and quality checks to keep labelling costs under control while maximising model performance.
Training & tuning
Using best‑practice architectures and tooling, we train or fine‑tune models and compare variants using a robust evaluation suite.
Deployment & monitoring
We wrap models in APIs or services, add observability, and create feedback loops so performance improves as your users interact with the system.
Ready to get started?
Let's discuss how data training can transform your AI models and business outcomes.
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