Features
Start from just a small sample
You don’t need expansive datasets to get your own fine-tuned and custom-trained AI models anymore.
Upload a small dataset, or even just write a few examples. Then use our enhance feature to turn that initial sample into a viable dataset to train your model on. A custom model generates high quality optimised variations to expand the samples to large enough sets for fine-tune training.
Choose from a pre-processed dataset
Consider using one of our many optimised dataset templates and get started right away.
We made a bunch of ready-made datasets spanning a variety of fields that you can choose from and use as a template to start without uploading your own data.
Start from a pre-trained model
The fastest way to get started with custom trained AI models. Pick one of our pre-trained models that are already experts in their own fields.
Maybe you don’t need a super specialised model, but just one that’s more optimised and less gigantic than other big models. A pre-trained model could be your solution.
Fine-tune a Llama 3 model
Create your own AI models with your own (enhanced) data and fine-tune it to think and act how you want it to.
There are countless examples of use cases where fine-tuning would be preferred over conventional models. In many instances models like ChatGPT or Gemini would be costlier, too unpredictable, slower, or simply much bigger than they need to be. This is often seen in scenarios where ChatGPT is being constantly API called for relative simple tasks, like checking grammar. A fine-tuned model could thrive here.
Test it
We have various methods to test your custom model, so you can play around and get know your model before continuing to deployment.
After training and fine-tuning your model, of course you want to know how it performs. We created different ways to get insight into your model and it’s characteristics, like simply chatting with it in our interface.
Deploy with an API
Deploying is now made easy with our ready to use platform so you can scale without the need for GPU’s.
We support multiple model deployment at scale. More accessible than ever, you can run multiple models without needing costly GPU’s and complex infrastructure. We did all the hard work for you.
Learn as you go
Enable continuous learning mode to improve the model over time, automatically.
By continuously giving back usage data to the model it can learn from what it’s doing. This way you can start immediately and over time it can only get better and better.
