Kili Technology is a Paris data-labeling platform that helps enterprises build high-quality training datasets for AI, with strong QA and project tooling.
Kili provides a labeling workspace for images, text, audio and documents, plus workforce management and quality-control features that keep annotation error rates low. Enterprises use it to create the ground-truth data their models depend on — from document extraction to medical imaging — and Kili’s review workflows measure and improve annotator agreement. The platform integrates with storage and training pipelines so data flows cleanly into model development.
Kili is sold through enterprise contracts based on seats, data volume and services; pricing is established with its sales team.
ML teams in finance, healthcare, manufacturing and mobility that need production-grade labeled data with measurable quality.
Kili serves enterprises whose models are only as good as their labeled data.
Import data, configure the labeling interface for your schema and run a pilot batch; Kili’s quality tools immediately highlight ambiguous cases.
Teams get cleaner ground truth and measurable annotator agreement — the unglamorous work that decides model quality.
Onboarding covers schema design, labeling interface setup and quality workflows; Kili’s team helps structure large projects and reviewer pools.
Labeling quality still depends on clear guidelines, and complex projects need ongoing QA attention.
ML teams say the quality tooling is what separates Kili from basic annotation editors.
Models learn from labels, so labeling quality is model quality. Kili’s QA workflows catch the disagreements that quietly poison datasets. Teams building high-stakes vision or document AI will find the discipline pays for itself at evaluation time.
No. Kili is a commercial platform; trials can be arranged for teams evaluating labeling workflows.
Yes — dataset creation, pre-labeling and export are available through APIs for automation inside ML pipelines.
Against labeling marketplaces, Kili emphasizes quality measurement and enterprise project control; against simple annotation tools, it manages the full data workflow.