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AI GitHub companion that summarizes PRs, answers questions and proposes fixes inside issues and pull requests.
AI SQL analyst that learns your database to turn plain-English questions into queries, dashboards and reports for teams.
AI coding assistant for editors and IDEs that explains, refactors, documents, and generates code across 56 languages.
AI SQL toolkit for analysts and developers to generate, optimize, validate, format and explain queries across 30+ database engines.
No public pricing
No public pricing
Free trial available
Free trial available
Free trial available
- ✦Chat inside GitHub issues and PRs
- ✦Task-to-implementation plans with code
- ✦Automatic bug-fix suggestions
- ✦Pull-request summaries for faster review
- ✦Full-codebase context
- ✦GitHub-native integration
- ✦Fast tensor operations
- ✦Differentiable tensors for gradient-based optimization
- ✦Network connectivity
- ✦Integration with Bun and Flashlight
- ✦Support for GPU computation with CUDA (Linux) and CPU computation (macOS)
- ✦Auto-learns database schema without manual setup
- ✦Natural-language querying with accuracy scoring
- ✦Drag-and-drop personal dashboards
- ✦Automated PDF and interactive reports
- ✦Works inside ChatGPT, Claude, Slack and MS Teams
- ✦Desktop app keeps query results local
- ✦Bug detection and fix suggestions
- ✦Code and CSS framework conversion
- ✦Unit test and documentation generation
- ✦Regex, SQL query, and CI/CD pipeline generation
- ✦Code explanation and style checking
- ✦Editor extensions for VS Code, Sublime, JetBrains, Visual Studio
- ✦Natural-language to SQL/NoSQL query generation
- ✦AI-driven query optimization with rewrite suggestions
- ✦Syntax validation with automated error fixes
- ✦Query formatting and cross-engine conversion
- ✦Schema-aware data source connections with autosuggest
- ✦Rule-based guardrails per connected data source
- ✦Support for large schemas with 900+ tables
- →Speeding up pull-request reviews
- →Implementing features from task descriptions
- →Debugging with AI-proposed solutions
- →Answering questions about a repo
- →Boosting a solo developer's output
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Let non-technical staff self-serve data insights
- →Speed up ad-hoc analysis for BI teams
- →Generate recurring reports automatically
- →Query across many SQL databases and warehouses
- →Generating unit tests for existing functions
- →Refactoring legacy code to modern practices
- →Producing inline documentation automatically
- →Learning new programming languages or concepts via AI explanations
- →Analysts writing SQL without deep query-syntax knowledge
- →Developers debugging and optimizing slow queries
- →Teams standardizing SQL formatting across a codebase
- →Migrating queries between database engines
- →Learners wanting plain-language explanations of SQL statements