Defog is a natural language data query platform that utilizes fine-tuned large language models to enable users to ask free-form questions about their data, regardless of its source. This technology eliminates delays in data retrieval, allowing users to obtain immediate insights and explore complex queries without navigating through traditional filtering methods.
Funding
$2.7M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Business users often face delays in accessing and analyzing data due to the need for specialized technical skills and complex querying processes. Traditional data retrieval methods require navigating through multiple filters or relying on data analysts, creating bottlenecks and hindering timely insights.
Solution
Defog provides a natural language data query platform that allows users to ask free-form questions about their data, regardless of its source, and receive immediate answers. By leveraging fine-tuned large language models, Defog eliminates the need for complex SQL queries and technical expertise, enabling users to explore hypotheses, drill down into specifics, and dive deep with follow-on questions. The platform connects to various data sources, including databases and CSV files, and adapts to user feedback to improve response accuracy. Defog empowers users to obtain data-driven insights without waiting for assistance from data analysts.
Target Audience
Defog is designed for business users, data analysts, and decision-makers who need quick and easy access to data insights without requiring specialized technical skills.
Features
- Natural language interface for querying structured data
- Integration with databases such as Postgres and Snowflake
- CSV file support for querying data without a database
- Fine-tuned large language models for accurate text-to-SQL conversion
- SQLCoder, an industry-leading AI model for querying structured data
- Data privacy: user data is never shared with anyone, including the AI model
- Adaptive learning: Defog learns from user feedback and preferences
- SQLEval, an extensible, open-source evaluation framework to measure the accuracy of the models