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.

QUANTITYORDEREDORDERDATEADDRESSLINE1ADDRESSLINE2DEALSIZE
302/24/2003 0:00897 Long Airport AvenueSmall
345/7/2003 0:0059 rue de l'AbbayeSmall
417/1/2003 0:0027 rue du Colonel Pierre AviaMedium
458/25/2003 0:0078934 Hillside Dr.Medium
4910/10/2003 0:007734 Strong St.Medium
3610/28/2003 0:009408 Furth CircleMedium
2911/11/2003 0:00184, chausse de TournaiSmall
4811/18/2003 0:00Drammen 121, PR 744 SentrumMedium
2212/1/2003 0:005557 North Pendale StreetSmall
411/15/2004 0:0025, rue LauristonMedium
372/20/2004 0:00636 St Kilda RoadLevel 3Medium
234/5/2004 0:002678 Kingston Rd.Suite 101Small
285/18/2004 0:007476 Moss Rd.Medium
346/28/2004 0:0025593 South Bay Ln.Medium
457/23/2004 0:0067, rue des Cinquante OtagesMedium
368/27/2004 0:0039323 Spinnaker Dr.Medium
239/30/2004 0:00Keskuskatu 45Small
4110/15/2004 0:00Erling Skakkes gate 78Medium
4611/2/2004 0:007586 Pompton St.Medium
4211/15/2004 0:00897 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.