A model that reads a policy number as a date, or a complaint as praise, fails quietly. In Kili, the underwriters, recruiters and clinicians who know your text confirm or correct its entities and categories, for AI evaluation, post-training and text annotation, while Consensus shows how far they agree.
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Find where your NLP model misreads your domain before your customers do. The underwriters, recruiters and clinicians who know the vocabulary confirm or correct its entities and categories, imported as predictions tagged with the model's name. Their labels come back through the SDK, ready to compare with the model and retrain it.
Sentiment, intent and entity boundaries are judgment calls, and you can see how consistent those calls are. Consensus scores how far labelers agree on each entity class or category, Honeypot tests them against answers you trust, and review steps send a sample to senior specialists. Weak spots show up before they reach training data.
CVs, claims and patient notes don't have to leave storage you control. Keep files in your own AWS S3, Google Cloud Storage or Azure Blob bucket while you use Kili's cloud, or install Kili on your own Kubernetes, with no internet access needed after installation. Every label version keeps its author, label type and date.
Speak to our team for a free 14-day evaluation.
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.
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Got questions about Kili Technology? Check out our FAQ. If you can't find it in this list, drop a question for our team.
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