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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.
[1]
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.
[2]
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.
[3]
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.
Discover the best option for your data labeling needs
Free Trial
Image, video, text, PDFs, large images & rich text labeling interfaces
100 annotations
Grow
All Free features plus:
Zero-shot Labeling Co-pilot
Instant quality review scores
Upload via URL and Cloud
Support level adapted to your needs
Accessible professional services
Enterprise
For companies with enterprise needs and specific data security requirements
Custom Price
All Grow features plus:
Dedicated customer success representative
Professional services
Custom contract & terms
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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.