Training data,
hand-picked.
Send us the data your model needs. We label images, video, footage, technical material, text, audio, UI screens, forms, and training datasets with human review, clear QA states, and reviewed labels ready for production handoff.
send a sample and a target schema — the first batch is shaped around the format your team needs
labels delivered into production workflows
review visibility on every finished batch
review capacity available for mixed data
days, typical pilot to first reviewed batch
Pick the frame.
Six task types we can run as a pilot. Flip through the frames and use the one closest to your data as the starting point.
Proof in motion.
Two playable clips from completed output, followed by additional frame studies in the same preview format.
Object tracks through footage.
A short finished clip with timing, movement, and review-ready overlays kept aligned across the frame sequence.
- track IDs stay readable through movement
- overlays stay aligned to the active frame
- review states stay visible before handoff
Per-frame visual annotation.
Finished footage that shows how label overlays, timing, and QA states can be inspected before delivery.
- label overlays stay visible during playback
- timing remains preserved in the clip
- review states remain easy to inspect
image, point-cloud, document, and audio previews in the same frame language
drag or scroll sideways
How raw data becomes usable.
Five steps keep the work clear from first sample to reviewed handoff.
guidelines versioned like code
review states attached to each batch
A working session with your team, then we write the spec you wish you had: edge cases named, illustrated, and versioned like code.
Examples are calibrated before production work starts. You see where ambiguity lives before scale begins.
Labels move through the agreed schema with in-tool QA. Ambiguity is escalated, resolved, and added back to the working spec.
A review pass checks consistency, resolves questions, and keeps finished work ready for production handoff.
Labels, notes, metadata, and review status arrive in the format your engineering or AI pipeline needs.
Clear operations. Clean labels.
Mixed data workflows
Images, footage, documents, text, audio, UI screens, and training sets can stay inside one clear labeling workflow.
Review built in
Every batch gets a visible review path, so questions, edge cases, and approvals stay attached to the work.
Schema discipline
Edge cases are named, resolved, and kept with the project instructions so later batches stay consistent.
Exports fit the pipeline
Delivery is shaped around the tools and formats your pipeline already uses.
No model outgrows the quality of its labels.
Care at the bottom becomes intelligence at the top.
Hand-picked,
delivered.
Tell us what needs to be labeled, how it should be reviewed, and where the finished data needs to go.
project scope by email · replies within one business day · NDA on request