Connect your data sources, set up data views with SQL, configure a GPT Assistant, create a Custom ChatGPT, and share it with users, employees, or customers.
DataLang's Core Features
Natural Language Processing
AI-Powered Insights
Data Analysis
Interactive Querying
Intuitive Data Exploration
AI
DataLang's Use Cases
#1Exposing a specific set of data using SQL to train GPT and facilitate natural language conversations with the database
#2Seamlessly transforming database information into accessible and secure API endpoints
#3Having interactive conversations with the database using natural language queries
#4Creating tailored GPT models from the data for customized insights and interactions
FAQ from DataLang
Do I need any technical knowledge to use DataLang?
You need to know your connection string (e.g. postgres://{USER}:{PASSWORD}@{HOST}:{PORT}/{DATABASE}) and perform SQL queries. Once you have your data views set up, you can ask questions in natural language without needing any technical knowledge.
How does DataLang secure my data?
DataLang ensures data security through appropriate measures such as encryption and access control. It prioritizes the protection of user data.
What are some use cases for DataLang?
DataLang can be used for interactive querying, data analysis, creating custom GPT models for insights, and transforming data into API endpoints.
What types of questions can I ask in DataLang?
You can ask any natural language queries related to your database, such as retrieving specific data, analyzing trends, or obtaining insights.
DataLang Support Email & Customer service contact & Refund contact etc.
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