Bad labels get caught at the step that produced them, not in a cleanup pass three weeks later. Quality is constructed while the work happens, and the reviewer has enough context to make the correction right the first time.
.svg)
.svg)
.svg)
Two specialists looking at the same asset will disagree wherever your criteria are unclear, and that is worth knowing in week one rather than at delivery. Kili puts a number on the gap and breaks it down by class and by person, so you fix the instruction rather than the batch.
Measure every labeler against work you already know is right, and a contributor drifting from your standard shows up as a score instead of a complaint from a downstream team. You find out while it is still one person's calibration.
A reviewer guessing at why a label is wrong produces a second wrong label. Kili puts the model's prediction, the class definition and the labeler's own question in the same frame, so the person making the correction knows what the disagreement was actually about.
Speak to our team for a free 14-day evaluation.
.webp)
An evaluation set is only as good as the agreement behind it. Run the same assets past several specialists, watch where they diverge per class, and fix the criteria before the benchmark is frozen. Honeypot holds the set against labels you already trust, so drift in the judges shows up as clearly as drift in the model.
Explore AI Evaluation
Post-training data is an argument about what a good answer looks like, and the argument is usually inside your own team. Consensus makes it visible: two specialists write against the same ontology, and the gap between them is a number you can act on. Tighten the criteria, re-run the step, and the disagreement closes.
Explore Post-Training
When the class is a domain call, volume is where quality quietly slips. Set the share of assets that go to more than one labeler and keep honeypots on the step that matters. The review step catches what those two miss. Per-person scores point at the contributor who needs the criteria explained, which a project-level average never does.
Explore Data Labeling
Kili Technology is a complete data suite that supports all data types and handles specialized formats for domain-specific requirements.
Customer stories from the programs that put this to work — with the numbers behind them.
.png)
.png)
.png)
Stay up to date with fresh content from our team — tutorials, use cases, and ideas to help you train AI/ML models better.
Got questions about Kili Technology? Check out our FAQ. If you can't find it in this list, drop a question for our team.
%3Aformat(webp).webp)
%3Aformat(webp).webp)
.png)
