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All-in-one no-code platform combining forms, workflow automation, AI agents, a database and email in a single subscription.
AI coding assistant for editors and IDEs that explains, refactors, documents, and generates code across 56 languages.
VS Code extension letting developers chat with their own custom OpenAI assistants without leaving the editor.
Atlassian's Git repository hosting for teams with built-in CI/CD pipelines and tight Jira integration for code review and deployment.
No public pricing
Free trial available
No public pricing
No public pricing
Free trial available
- ✦Drag-and-drop form builder with conditional logic
- ✦Workflow automation with 60+ node types and 400+ integrations
- ✦Prebuilt and custom AI agents for tasks like lead scoring
- ✦Relational database with AI-enriched columns
- ✦Drag-and-drop email builder with AI-drafted content
- ✦Company and contact enrichment and web research tools
- ✦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)
- ✦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
- ✦in-editor chat with OpenAI assistants
- ✦workspace source-code context sharing
- ✦support for custom, user-defined assistants
- ✦secure management of the user's OpenAI account
- ✦Git repository hosting
- ✦Bitbucket Pipelines CI/CD
- ✦Pull requests and code review
- ✦Native Jira integration
- ✦Branch permissions and access controls
- ✦IP allowlisting and security features
- →Capturing and automatically routing sales leads
- →Building onboarding or support-triage workflows
- →Running AI-driven lead scoring and qualification
- →Sending personalized, data-merged email campaigns
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →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
- →getting coding help without switching out of VS Code
- →using a personalized OpenAI assistant tuned to a project
- →quick in-editor Q&A while writing code
- →Source code management
- →CI/CD automation
- →Team code review
- →DevOps for Jira-based teams