We focus on high-quality AI model training data
We help teams prepare, refine, and structure data for large language models. Our workflows emphasize accuracy, consistency, and delivery speed so your models learn from reliable examples.
Label sentences or documents (e.g., sentiment, topic). Tag entities (names, places). Create Q&A or instruction–response pairs.
Assign image-level labels (cat, car). Draw bounding boxes around objects. Create segmentation masks (pixel-level labeling).
Label actions across time (running, cooking). Mark start/end timestamps of events. Track objects frame-by-frame.
Transcribe speech → text. Label speakers (who is talking). Tag sound events (noise, music, siren).
Assign target label per row (class or value). Clean/verify structured fields. Handle missing or inconsistent labels.
Label sequences over time (trend, anomaly). Mark events or spikes at specific timestamps. Assign future prediction targets.
Label nodes (user, product type). Label edges (relationship type). Assign graph-level labels (e.g., fraud network).
Rank outputs (better vs worse). Give scores or preferences. Define reward signals (success/failure).
Align data types (image ↔ caption). Label cross-modal relationships. Annotate combined tasks (image + question → answer).
Label sentences or documents (e.g., sentiment, topic). Tag entities (names, places). Create Q&A or instruction–response pairs.
Assign image-level labels (cat, car). Draw bounding boxes around objects. Create segmentation masks (pixel-level labeling).
Label actions across time (running, cooking). Mark start/end timestamps of events. Track objects frame-by-frame.
Transcribe speech → text. Label speakers (who is talking). Tag sound events (noise, music, siren).
Assign target label per row (class or value). Clean/verify structured fields. Handle missing or inconsistent labels.
Label sequences over time (trend, anomaly). Mark events or spikes at specific timestamps. Assign future prediction targets.
Label nodes (user, product type). Label edges (relationship type). Assign graph-level labels (e.g., fraud network).
Rank outputs (better vs worse). Give scores or preferences. Define reward signals (success/failure).
Align data types (image ↔ caption). Label cross-modal relationships. Annotate combined tasks (image + question → answer).
Define the overall scope and requirements of the data annotation project to ensure alignment with client or internal expectations.
Receive and prepare raw data to make it suitable and ready for annotation while ensuring consistency and data integrity.
Create clear and detailed annotation guidelines to ensure consistent and accurate labeling across all annotators.
Select and configure the annotation tool that best fits the project requirements and data type.
Conduct a small-scale pilot annotation to validate guidelines, tool setup, and annotator understanding before full execution.
Perform full-scale data annotation following approved guidelines and refined processes from the pilot phase.
Review annotated data to ensure it meets quality standards and provide feedback to annotators for correction and improvement.
Conduct final checks to confirm data completeness and quality before delivering the annotated dataset to the client or downstream team.
Have a project idea where we can add value? Share the details with us and let's explore how we can bring your vision to life.
Our team reviews every inquiry and usually responds within 1–2 business days.
To know more about our services and workflow, please visit theservices page
A combined team of software engineers, data scientists, ML engineers, and prompt specialists who support end-to-end LLM training, instruction tuning, coding tasks, and AI system evaluation.
Creative writers and multilingual specialists with strong linguistic and cultural expertise, delivering high-quality annotations for natural language understanding and generation tasks.
Industry professionals with practical experience across finance, operations, marketing, and enterprise workflows, ensuring business relevance and real-world accuracy in annotations.
Scientists and engineers with academic or industry backgrounds in fields such as biology, physics, chemistry, and mathematics, ensuring precise annotation of technical and scientific data.
Certified healthcare professionals and biomedical specialists who provide medically accurate and compliance-aware annotations for health and life sciences AI applications.
Legal professionals and compliance specialists trained to annotate contracts, laws, and regulatory materials with precision, supporting AI systems in high-risk and regulated environments.
Experts in psychology, sociology, economics, and political science who deliver context-rich annotations rooted in human behavior and societal dynamics.
Specialists trained in labeling images, audio, and video data, supporting computer vision, speech recognition, and multimodal AI systems.
Senior reviewers and evaluators responsible for ensuring annotation accuracy, consistency, and AI safety through validation, audits, and LLM behavior assessment.
Join our amazing team to contribute to our ongoing and upcoming projects. We are always looking for passionate individuals to help us build the future.
Send us your resume, and our hiring team will reach out if there's a strong fit.
To know more about our career options, please visit thecareers page
### We offer data annotation services for SFT and RLFH to assist in LLM training.