A model that passes an automated benchmark can still be wrong in ways only your specialists would catch. Kili puts those specialists in charge of the verdict, scores how well they agree, and keeps the record, so you ship on evidence rather than on a leaderboard.
Generic benchmarks tell you how a model does on someone else's questions. Your specialists judge it on yours: each output scored against the criteria that decide whether it is usable in your domain, such as accuracy, completeness or compliance, with a written reason wherever a score needs one.
An evaluation score is only as trustworthy as the people who produced it. When the same outputs go to several evaluators, Kili shows where they split, criterion by criterion. Each evaluator can also be scored against verdicts you already trust, and nobody signs off their own grading.
When a model ships on an evaluation's say-so, someone will ask who judged it and why. Kili keeps every verdict and written reason under its evaluator's name, with the date and time. A review adds a version instead of overwriting, so an automated judge's score and your expert's correction both stay on record.
The prompts worth evaluating on are real ones: customer records, patient notes, internal policy, and everything your model said about them. That material can stay in your own cloud storage while you use Kili's cloud, or stay entirely inside your perimeter, with Kili installed on your own Kubernetes.
Speak to our team for a free 14-day evaluation.
A retrieval-augmented assistant can quote the right policy and still answer the wrong question. Your specialists see the retrieved passages, the question and the model's answer together, and judge retrieval and generation as separate criteria, so a failing score points at the retriever or at the generation step, not at "the model" in general.
See how Kili works for your use case
When a model pulls fields out of claims files, contracts or ID documents, one wrong value can flow straight into a payment or a compliance decision. The model's extraction arrives pre-filled on the document, your reviewers confirm or correct each field, and every correction is kept beside the model's original value, so you can measure where it goes wrong.
See how Kili works for your use case
A defect-detection model that scores well offline still has to be right on your parts, your lighting and your grades. Its predictions arrive pre-loaded on the images, your quality engineers accept or correct each one, and every correction stays beside the model's original annotation, so you can see which defect classes it misses before it runs unsupervised.
See how Kili works for your use case
Evaluation rarely stops at one model. When the next team needs call transcripts checked, video predictions reviewed or post-training data written by the same specialists, it starts as a new project in the platform your security team already approved, across text, documents, images, video, audio and geospatial data, with the same review steps, roles and deployment.
Tell us about your use case
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)
.png)
%3Aformat(webp).webp)
.png)
.webp)