Turn pixels into useful labels.
What we deliver
Give each data type the structure it needs, with shared guidelines and human review built into the work.
Mark the objects, regions, and details your vision model needs to learn. Annotation rules and sample review keep labels consistent across a dataset.
Annotate video frames with labels that preserve movement and continuity. Define frame sampling, tracking rules, and event boundaries around your task.
Organize documents and conversations with a clear text taxonomy. Human review helps resolve ambiguous wording and apply labels consistently.
Label speech and non-speech audio with the timing and context your models require. Capture speaker turns, sound events, and uncertain segments clearly.
Prepare image and document labels that help OCR systems read text and understand layout. Capture text regions alongside their order and structure.
Structure point clouds and 3D scenes with labels suited to perception tasks. Define object classes, box conventions, and review rules for consistent spatial data.
Label prompts and generated responses against a shared rubric. Capture intent, quality, safety, and response traits so examples can support training and evaluation.
How we work
Requirements agreed early. Human review throughout.
Agree on the taxonomy, attributes, edge cases, and export schema.
Review a small sample together to align labeling decisions.
Apply the guidelines and resolve uncertain examples during review.
Validate label structure and package files for your pipeline.
Tell us about your data format, annotation schema, tools, and expected volume.
Have a data project?
Tell us what data you need.
We’ll help you plan the next steps.