Kellar
Your data, faked. faked. faked.
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.
| PRODUCTCODE | ADDRESSLINE2 | POSTALCODE |
|---|---|---|
| S10_1678 | 10022 | |
| S10_1678 | 51100 | |
| S10_1678 | 75508 | |
| S10_1678 | 90003 | |
| S10_1678 | ||
| S10_1678 | 94217 | |
| S10_1678 | 59000 | |
| S10_1678 | N 5804 | |
| S10_1678 | ||
| S10_1678 | 75016 | |
| S10_1678 | Level 3 | 3004 |
| S10_1678 | Suite 101 | 10022 |
| S10_1678 | 94019 | |
| S10_1678 | 97562 | |
| S10_1678 | 44000 | |
| S10_1678 | 51247 | |
| S10_1678 | 21240 | |
| S10_1678 | 4110 | |
| S10_1678 | 70267 | |
| S10_1678 | 10022 |
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.
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.
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.
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.