Model pre-labelling
Existing or bootstrapped models propose the first pass.
Model-in-the-loop, human-in-charge
AI-Assisted Labeling
Pre-labelling accelerates delivery. Human review decides what ships.
Faster, Without Trusting the Machine
Model-assisted labelling is the biggest available saving in an annotation budget and the easiest way to poison a dataset. The saving is real when a human corrects every pre-label; the poisoning happens when confident, wrong suggestions get accepted because accepting is quicker than fixing.
What Is Included
Interactive segmentation
Click-to-segment tooling for pixel-level work.
Auto-tracking
Object identity propagated across frames, then verified.
Active learning
The next batch is chosen by what the model is worst at.
Weak supervision
Rules and heuristics bootstrap a first dataset.
Synthetic augmentation
Generated data used to fill known gaps, clearly marked as synthetic.
How We Work
- Every pre-label is reviewed Nothing reaches a delivery because a model was confident.
- Correction rate is tracked How often humans overrule the model tells you whether it is helping.
- Bias watch Pre-labelling can entrench a model's existing errors; sampled blind labelling checks for it.
- Assisted and manual reported separately You always know which part of a delivery was accelerated.
The Controls Behind It
- Confidence thresholds Low-confidence suggestions are hidden rather than shown as answers.
- Blind control batches A share is labelled without assistance to measure the effect.
- Model versioning Which assisting model produced which pre-labels is recorded.
- Regression checks Assisted output is measured against gold sets every cycle.
Ready When You Are
Tell us what you are building and we will come back with a plan, a sample and a price.