Defog

Runs natural language data queries on customers' own servers, ensuring data privacy and security while providing accurate and trusted answers to complex questions.
Natural Language Query Enterprise SQL Data Privacy Solutions

Defog is a platform that uses fine-tuned AI models to run natural language data queries running on customers' own servers. That means data can be kept private and secure while still getting useful answers to queries. Defog is geared specifically for enterprise SQL, so customers get answers they can trust even when they need to ask complicated questions.

Among Defog's features are:

  • Accurate Results: Defog's training and inference engine produces results that capture explicit rules from database schema and user feedback.
  • Customizable Interface: The platform has an infinitely customizable user interface, letting customers visualize data and ask follow-up questions.
  • Data Privacy: Defog doesn't touch or move customers' data and offers on-prem options and hard filters for row-level access control.
  • Supported Databases: Defog works with all major SQL databases and data warehouses.
  • Statistical Modeling: Defog can create statistical models, including running t-tests, regressions and more.

Defog is based on SQLCoder, an open-source model for text-to-SQL generation that does far better than other top models when it comes to out-of-training set SQL schemas in Postgres. SQLCoder has 99%+ accuracy when fine-tuned on individual database schema.

Defog pricing includes a free community version with limits on cloud queries and tables. For bigger businesses, an enterprise plan offers unlimited queries and tables, model fine-tuning, human-in-the-loop feedback, and customizable front-ends with on-prem or cloud hosting options.

Defog is geared for enterprises that want to free up time and money by automating data queries and analysis. By using powerful AI technology, Defog automates the process of generating SQL queries, so data teams can get answers to business user requests more quickly and accurately.

Published on June 13, 2024

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