Auto-Labeling powered by machine learning models to increase data annotation
Quick onboarding from guideline creation to annotation
Proprietary datasets and pre-trained models to speed up project initialization
Enterprise ready
On-premise data, integrations in AWS Sagemaker, GCP Vertex, Azure ML, and more
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For enterprises that work in environments without internet access, Labellerr offers a fully on-premise solution. The software can be deployed either in your cloud infrastructure or on your own hardware
Labellerr allows you to upload data to its encrypted storage through UI, CLI, or Python SDK. The uploaded data can be versioned, used for model training, model versioning, and deployment. All data or models created through Labellerr’s encrypted storage solely belong to its creator
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Transparency
Gives users power to manage and govern the training data project in single view
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Full visibility of the project thanks to highly interactive and analytics rich dashboard
User level authorization to supervise the annotation workflow
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Multiple method to check the accuracy and pinpoint the problem at each data point level
Setup Alerts on mobile and email to keep track of labeling quality
Best price in the industry
Multiple pricing model best suited to your requirement. No unnecessary long term contract. Feel free to use for small proof of concept or large scale long term project
We offer immediate access to 50+ pre-labeled datasets allowing you to jump-start and accelerate your AI projects.
Data collection
We have extensive experience with large scale custom data collections covering audio, image, testing, sentiment and point of interest. Utilize our global crowd workforce and secure locations to source new and unique training data, allowing you to train your models on data specific to your use case and target markets
Synthetic data
Leverage our data products and expertise to artificially generate data enabling you to access difficult to obtain or edge case data.
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