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Train and deploy GPT models for any usecase with the first no-code AI platform for businesses.

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Why Us

Range of open-source models

Unlock the full potential of your projects with our diverse range of open source models. We provide you with a comprehensive collection of cutting-edge models to drive innovation.

Train/fine-tune/deploy with no-code solution

Quickly train and test different solutions and perform experiments without a team of data scientists and devops engineers.

Bring your own cloud for better security

Worried about big tech stealing your data? With our platform you can bring your own cloud for better security.

Get Customized models from our marketplace

Don’t want to train

cost optimization

We internally allocate resources for computation(CPU + GPU clusters) in an optimized way. Saving $$ for your organisation.

No hussle for deployment and scaling

Our no-code solution handles all your models’ deployments and scaling.

Frequently Asked Questions

Quick Answers to questions you may have. Still can’t see what you’re looking for, let us know

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Please fill the form on the website and one of our team member will contact you. Currently product is in beta, hence we will solve your use case by assigning a data scientist to you(service based model. Once the product is released we will provide you the access.
$10/$12 million dollars are required if we train a model like gpt-3 from scratch. Which means the following.
1.  Scrapping the internet(Like 10%-20% of the internet).
2.  Training the model for months.(2-3 months on super computers).
3.  Deploy the model on the cluster of servers.(It takes huge amount of money to load and run a model like chat-gpt).
 
The above mentioned thing is only possible if you are a big giant(microsoft) or specialized in creating new models(OpenAI). These companies then sell the model on subscription basis to the users(companies). We don’t not do this.
 
1.  We take an open source model(free model) often called as foundational model.
2.  Then we fine tune it to our requirements(this does not requires millions of $ or months).
3.  We deploy it on our server and scale it as per the requirement.
 
Every one in the industry uses the same method to solve their usecases. It is also the recommended way.

Deepspeed, Ray, and Ludwig are libraries used for training, customizing and deploying models.
We’re building a user-friendly solution on top of these libraries, similar to no-code tools like Jenkins/bubble. While it’s possible to accomplish the same tasks using the libraries alone, but a UI based solution is still popular because it reduces effort and costs atleast 100x. Likewise, our no-code tool, built upon these open-source libraries, will significantly save time and money.

This solution will help businesses to develop any solution based on llms(e.x. chatgpt) end to end. Our solution is like a top layer sitting on your cloud providers which will manage everything for you. We will take care of the following.
 
1.  Data preprocessing(for every use-case).
2.  Model training(any cloud provider)
3.  Model deployment(any cloud provider).
4.  Model monitoring.

Our business model is a subscription based model. We would offer different tiers of pricing in which different features would be activated.
The main feature/parameter would be based on number of hours of training a person has done using our platform.
There would be other factors too in the pricing bracket. Ex.

1.  Number of users.
2.  Number of pipelines/experiments/workspaces.
3.  Number of deployments etc.

**Note: This is only a rough estimation.

The tech giants are making a platform for the same but it has several problems as follows:

1.  Data safety. They might use your data for training their own models.
2.  Cost. The servers are 3/4x costly if you use same servers through their service.
3.  No migration. You are not allowed to download the model and hence can’t use the power of multiple cloud.
4.  No domain specific models. We are going to make specific models for specific domain, which can be really helpful for the companies.

What is the pain points of the customers?

1.  Due to lack of people who have expertise on these LLMs(large language models) enterprises have no idea how to use these new AI into their systems.
2.  Data privacy issue. If we give our data to openAI/google’s vertex AI etc. is it safe?
3.  Google/AWS/Azure want to lock you into their own cloud. They charge 3x, 4x for the same servers if you are accessing the using their product. You can’t train/deploy on multiple clouds. Every company wants to be multi-cloud.
4.  Google/OpenAI never let you download the AI model.
5.  You need an expensive team of Data scientists to even understand and use these tools.
6.  A lot of client want specific solution not just AI. Client want to solve their problem they are not interested whether it requires AI or not. It might require LLMs( chatgpt )’s integration with various other APIs. This is missing in all tools.

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