DataLang

What is DataLang?

"DataLang" is a platform that allows users to create GPT assistants and custom GPTs directly from their databases. Users can set up their data sources, add data views through SQL scripts, configure GPT assistants, and then chat with these assistants or create custom GPTs. The platform offers features like transforming database queries into secure API endpoints, chatting with databases using natural language, publishing custom GPTs, and ensuring data security with encrypted connection strings. Users do not need technical knowledge beyond setting up their data views initially to use DataLang effectively.

Furthermore, DataLang provides various pricing plans tailored to different user needs, ranging from a free Basic plan for a simple chatbot to a Business plan for large teams and businesses with extensive data view requirements. Users can easily share their chatbots in multiple ways, such as through public URLs, embedding on websites, publishing to the GPT Store, or utilizing APIs for question interaction.

The platform uses technologies like React, Remix, Tailwind CSS, Plausible, YouTube, and GPT-4 to offer a user-friendly interface, secure data encryption, API integration, and streamline natural language processing, database querying, and data analysis processes.

Who created DataLang?

Datalang was created by a team behind DataLang. The company focuses on enabling users to create GPT assistants and Custom GPTs directly from their databases. Users can set up their data sources, add data views, configure GPT assistants, and chat with the built-in assistant or create custom GPTs. DataLang also provides features like transforming database queries into secure API endpoints, chatting with databases using natural language, and ensuring data security with encrypted connection strings. The platform offers various pricing plans tailored for different user needs and sizes.

What is DataLang used for?

• Create a simple chatbot with your database
• Transform database queries into secure API endpoints
• Chat with your data using natural language
• Deploy custom GPTs on the ChatGPT store
• Ensure data security with encryption for database connection strings
• Share specific Assistant tailored to a customer
• Publish chatbots to the GPT Store
• Securely encrypt connection string credentials
• Enable natural language querying of databases
• Create GPT assistants and Custom GPTs from databases
• Ideal for individual users or small-scale projects
• Designed for small teams and growing businesses
• For large teams and businesses with extensive needs
• Deploy custom GPTs on the ChatGPT store and share with users
• Ensure data security with encrypted connection strings
• Filter data for specific customers and create custom assistants
• Share chatbots via public URL, embed on websites, publish to GPT Store, or use API

How to use DataLang?

To use Datalang, follow these steps:

1. Set Up Your Data Source:

   • Configure your sources, which can include databases, files, and text.

2. Add a Data View:

   • Expose data from a specific source by setting up SQL scripts.

3. Configure Your GPT Assistant:

   • Select the data sources you want to train the GPT (chatbot) with.

4. Chat with Your Data:

   • Engage in natural language conversations with your data using the built-in Assistant or create a custom GPT.

5. Share Your Chatbot:

   • Share your chatbot in various ways: through a public URL, embedding it on your website, publishing it to the GPT Store, or by asking questions via API.

6. Security and Privacy:

   • DataLang ensures secure connection strings by encrypting your database credentials within the system, only decrypting them when needed for data operations.

7. Pricing Options:

   • Choose from different plans tailored to individual users, small teams, growing businesses, and large organizations, based on the number of users, data views, and support levels required.

8. Top Features:

   • Database to API transformation, conversing with your data in natural language, deploying custom GPTs to the ChatGPT store, and ensuring secure connection strings.

9. FAQs:

   • Learn about the technical knowledge required (primarily SQL queries), data security measures, and the types of questions you can ask in DataLang.

10. Tech Used:

• DataLang leverages technologies like React, Remix, Tailwind CSS, Plausible, YouTube, and GPT-4 to provide a user-friendly interface for secure data analysis and integration.

By following these steps and leveraging the features and security measures offered by DataLang, users can effectively interact with their databases and extract valuable insights through natural language conversations.