Models produce outputs that look right, and only your experts can tell correct from plausible. Kili brings them into a designed review process and keeps the evidence of every call they make, so you can trust your AI and prove it.
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Your clinicians, underwriters and quality engineers spend their limited time judging, not learning a tool, so you actually get their input, and at scale. They score model outputs against criteria you define and explain their calls in free-text fields, while custom shortcuts and built-in OCR take the clicks out of the work.
Expert judgment stays consistent because it is designed into the workflow, not left to individuals. You decide where each level of expertise steps in, from consensus on the same asset to a second review by domain experts, and when criteria change, admins update the ontology and review steps without code.
You can show, not just claim, that experts reviewed the data and how well they agreed: the evidence a risk committee, regulator or customer asks for. Every decision records who made it and when, what the expert changed from the model’s output stays visible, and agreement is scored per class and per person.
Speak to our team for a free 14-day evaluation.
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Advisor messages, summaries, RAG answers, agent responses: generated content should only reach customers once someone qualified says it is right. Your business experts review samples of model outputs against criteria you define for accuracy, tone and compliance, their agreement is measured, and every verdict is kept, so a model that passes review has evidence behind it.
Explore AI Evaluation
The ground truth you train, fine-tune and test on is only as good as the people who validated it. Experts create and check golden datasets directly, including deliberate negative cases such as hallucination traps, with consensus showing where they disagree and review steps settling it, so the set holds up when the model is measured against it.
Explore Post-Training
Extraction from claims files, defect classes on the line, findings in imagery: labeling where the class is a domain call rather than a click. Across images, video, PDF, text and geospatial, experts flag ambiguities as they work, so first-level reviewers self-correct and instructions get clarified mid-project. The same review steps and scoring apply.
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.
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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.
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