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).
We align on goals, scope, and evaluation criteria for your LLM initiative.
specialized annotators apply precise guidelines for consistent training data.
We deploy and monitor your models in production environments.
Have a project idea where we can add value? Share the details with us.
We will get back to you within 1-2 business days.
Email:
sales@genmorphicsai.comJoin our amazing team to contribute to our on going and upcoming projects
Email:
career@genmorphicsai.com### We offer data annotation services for SFT and RLFH to assist in LLM training.