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.

PRODUCTLINEPRODUCTCODEADDRESSLINE1DEALSIZE
MotorcyclesS10_1678897 Long Airport AvenueSmall
MotorcyclesS10_167859 rue de l'AbbayeSmall
MotorcyclesS10_167827 rue du Colonel Pierre AviaMedium
MotorcyclesS10_167878934 Hillside Dr.Medium
MotorcyclesS10_16787734 Strong St.Medium
MotorcyclesS10_16789408 Furth CircleMedium
MotorcyclesS10_1678184, chausse de TournaiSmall
MotorcyclesS10_1678Drammen 121, PR 744 SentrumMedium
MotorcyclesS10_16785557 North Pendale StreetSmall
MotorcyclesS10_167825, rue LauristonMedium
MotorcyclesS10_1678636 St Kilda RoadMedium
MotorcyclesS10_16782678 Kingston Rd.Small
MotorcyclesS10_16787476 Moss Rd.Medium
MotorcyclesS10_167825593 South Bay Ln.Medium
MotorcyclesS10_167867, rue des Cinquante OtagesMedium
MotorcyclesS10_167839323 Spinnaker Dr.Medium
MotorcyclesS10_1678Keskuskatu 45Small
MotorcyclesS10_1678Erling Skakkes gate 78Medium
MotorcyclesS10_16787586 Pompton St.Medium
MotorcyclesS10_1678897 Long Airport AvenueMedium

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.