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Ship valuable Insurance AI models faster

Worry less about your data labeling infrastructure and focus on shipping your insurance models faster.

Flexible, fast, and secure, Insurance companies trust Kili Technology to help them build the highest quality training datasets for their models.

Trusted by Top Insurance Companies

Transform unstructured data faster

Less time spent fussing over data quality equals more time saved building your ML applications.

Label any dataset at scale


Label any dataset at scale

Rapidly and accurately label large volumes of data for insurance claims processing, risk assessment, customer service inquiries, and more. Automate labeling to save time with AI-powered tools and custom model predictions.

Achieve superior data quality for powerful models


Achieve superior data quality for powerful models

Use programmatic custom quality workflows, real-time collaborative tools, and advanced quality metrics to build the highest quality datasets for fraud detection, policy personalization, predictive analytics, and other critical models.

Secure and flexible data labeling solutions for insurance firms


Secure and flexible data labeling solutions for insurance firms

Cloud, on-premise, and hybrid data labeling solutions to meet the stringent data privacy of insurance companies. Customize team structures, roles, and access levels to ensure the management of sensitive data across various projects.


A professional workforce to scale faster

No labelers at hand? No problem. Kili has a global network of professional data labelers with expertise in multiple subjects and languages. Meanwhile, our data labeling platform allows you to retain full visibility and real-time quality control.

It was very easy to iterate the labeling process to correct the work of our AI when it was wrong. We were able to import predictions by the AI very quickly to the labeling interface, and the features and the shortcuts within Kili allowed us to modify them easily.

VincentArtificial Intelligence Director at an Insurance Company

With the choice of Kili, we are much more confident about the future. We decided to eliminate a large part of the technical debt by choosing a solution that will be perfectly mastered across a whole range of data science and AI projects.

Philéas Condemine
Philéas CondemineLead Data Scientist at Covea

It was an end-to-end solution for us: we got not only the very simple tool, but also a powerful API, high-quality experts as external labelers, and automated predictions in the process.

ViktorChief Data Officer at an Auto-Insurance Company

Discover the best option for your data labeling needs

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Free Trial

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For companies wishing to invest in their labeling operations

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For companies with enterprise needs and specific data security requirements

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Case Studies

Reducing technical debt with one solution
Case Study
Reducing technical debt with one solution

Covea used Kili Technology for their project of labeling 1.5 million customer verbatim. Today the pl...

Faster iteration for a more impactful model
Case Study
Faster iteration for a more impactful model

An insurance company used Kili Technology to efficiently iterate their model with advanced human-in-...

An end-to-end solution to answer all data labeling needs
Case Study
An end-to-end solution to answer all data lab...

Learn how to Kili Technology provided a full solution to an auto insurance company

Trusted by Data Scientists across Industries

Check out our guides

Fast-track Insurance AI
Fast-track Insurance AI

Learn how we tackle and overcome training data challenges in the insurance industry.

Efficient Key Information Extraction
Efficient Key Information Extraction

Extracting key information from text and documents is complex and challenging, not just due to the s...

Text annotation for NLP and document processing: a complete guide
Text annotation for NLP and document processi...

Text annotation is widely used in organisations to solve NLP tasks for machine learning models. Lear...

Frequently asked questions

What types of unstructured data can Kili Technology handle?

Kili Technology can efficiently help you label various data types from different sources:

  • Categorize text data from social media posts, emails, customer reviews, etc.

  • Extract information from PDFs from various documents such as police reports and medical reports, claim forms, and more

  • Detailed image annotation, satellite imagery labeling, and object detection for risk assessments, damage assessments, fraud detection, and various analyses

  • Video annotation for assessments, evaluations, and monitoring

Does Kili Technology provide a labeling workforce?

Yes, insurance companies can leverage Kili Technology's network of professional labelers who are specifically trained for various labeling needs in challenging domains. The professional service can be hired standalone through Kili SIMPLE, or as part of the Grow or Enterprise packages.

How does Kili Technology handle the security of propietary data?

Kili Technology is ISO27001 and SOC2 certified, and offers 3 secure deployment types depending on what your standards need: SaaS, Hybrid, and On-premise. Additionally, Kili Technology has sophisticated data bucket management tools so teams and members only access data relevant to their projects.

How does Kili Technology automate data labeling?

Kili Technology automates data labeling through the following:

  • Zero-shot labeling for text classification, named entity recognition, and object detection

  • A labeling co-pilot that uses a foundation model where users can craft prompts and leverage a very small amount of labeled data to label the rest of a much larger dataset automatically

  • Bring-your-own-model (BYOM): Users can leverage their own model to label a dataset and use human-in-the-loop review and correction to review and correct errors. These corrections can then be fed back into the model to improve accuracy.

  • Segmentation with SAM: Using SAM to do detailed image segmentation on Kili Technology

  • Active learning: A more classical approach in which users can start labeling data manually, feed these labels into a model, and have the model generate label predictions for the rest of the dataset. Labelers only need to review and correct errors and feed the data back to the model for fine-tuning.

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