Defog.ai: The Privacy-First AI Data Analyst
Defog.ai is a cutting-edge AI data analyst designed to provide accurate, adaptable, and secure data analysis, integrating seamlessly with various data sources while prioritizing user privacy.
What Defog.ai Does
Defog.ai enables users to query their databases and data warehouses using plain English, generating precise SQL queries and visualizing the results. This platform is powered by SQLCoder, an industry-leading large language model, ensuring consistent and reliable outcomes.Key Features and Functionality
Data Integration and Compatibility
Defog.ai supports a broad range of structured databases and data warehouses, including Snowflake, Postgres, SQL Server, MySQL, and more. It also connects with popular SaaS tools and allows the import of data from CSV and Excel files, making it versatile for various data sources.Privacy and Security
- Privacy-First Approach: The underlying data never leaves the user’s environment, and SQL queries are executed locally.
- Deterministic Security Filters: Every query is automatically tested and filtered for malicious keywords to prevent prompt injections.
- Self-Hostable: Defog can be hosted entirely locally, ensuring the highest level of security and privacy.
User-Friendly Interface and Deployment
- Desktop App: Run Defog on your desktop with minimal setup.
- Native Slack Bot: Interact with Defog directly within Slack.
- Cloud and Docker Deployment: Deploy Defog through AWS and GCP Marketplaces or use Docker for flexible and secure deployment.
Advanced Analysis Capabilities
- Multi-Step Reasoning: Pose complex questions and receive comprehensive, step-by-step answers.
- Statistical Analyses: Explore trends, correlations, and patterns in your data without specialized statistical software.
- Custom Analysis Tools: Create and customize analysis tools tailored to specific analytical needs.
Adaptability and Feedback
- Fine-Tuning: The model can be fine-tuned to specific use cases.
- Coachable: Guide Defog with feedback, ensuring it becomes smarter and more aligned over time.
- Database Readiness: Receive automated suggestions on improving database metadata for optimal use with Defog.
Technical Architecture
Defog’s architecture includes three main Docker images:- defog-docker-end-user: Handles user management, authentication, and provides UI elements.
- defog-backend: Converts user questions into prompts, stores metadata, and performs post-processing on LLM output.
- defog-vllm-onprem: The LLM service that generates SQL queries from prompts, built on PyTorch and FastAPI.