Kellar

Your data,

Build AI-driven models of your own data and generate limitless amount of fully realistic synthetic data, tuned just for your own specific use case. Build, host and use your models with simple cloud-based UI and API.

We are moving towards beta testing. Subscribe to our mailing list if you want to get involved.

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2S10_16782125557818NYCNY
5S10_167826.47.1555Reims
2S10_1678+33 1 46 62 7555Paris
6S10_16786265557265PasadenaCA
14S10_16786505551386San FranciscoCA
1S10_16786505556809BurlingameCA
9S10_167820.16.1555Lille
1S10_1678+47 2267 3215Bergen
2S10_16786505555787San FranciscoCA
14S10_1678(1) 47.55.6555Paris
1S10_167803 9520 4555MelbourneVictoria
7S10_16782125551500NYCNY
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1S10_167840.67.8555Nantes
6S10_16786175558555CambridgeMA
9S10_167890-224 8555Helsinki
5S10_167807-98 9555Stavern
1S10_16782155551555AllentownPA
1S10_16782125557818NYCNY

Solving data scarcity

In the era of Big data, controversially, data can feel like a scarce resource. Limited amount of relevant data can dramatically slow your application development, require you to do expensive data collection, or make your machine learning model dumber than it should be.

Efficent Application development

Modern web applications are built on top of real-world data. Without enought realistic data, your development mockups and prototypes do not communicate the right message to your test users, and understanding the value you are trying to provide may become unclear.

Comprehensive Testing

Building reliable software requires rigorious testing, and your tests require lot of data to cover enough corner-cases. Simple API-based generation of realistic test data fits well into your current CI/CD pipeline.

Smarter data-science

Most of the successfull data-science models are data-hungry, and collecting enough data might be possible only for large tech giants. With intelligent data synthetization, you can achieve large-scale representative dataset without expensive data collection.