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AI-powered code editor with agentic workflows for developers.
AI coding assistant that gathers project context to plan, generate, test and ship code across the SDLC via IDE and chat integrations.
Design-system platform that packages tokens, code components, and rules into scoped context for AI coding agents.
IDE coding assistant for VS Code and JetBrains that uses your own API keys across 15+ model providers, with agentic mode and autocomplete.
AI-powered IDE with code completion, generation, explanation and debugging, plus a cloud dev environment, for developers.
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No public pricing
- ✦AI-powered code completion and suggestions
- ✦Automated lint fixing
- ✦Cascade agent for advanced coding assistance
- ✦Integrated app building and deployment
- ✦MCP server support for custom tools
- ✦Terminal command integration
- ✦Memory of codebase structure and workflow
- ✦Automatic context-gathering from connected engineering sources
- ✦AI-generated code, tests and pull requests from tickets
- ✦Task planning that breaks complex work into subtasks
- ✦Auto-updating engineering documentation
- ✦Vector search over embedded project data
- ✦Multiple selectable AI models (GPT, Gemini, Claude, Llama, etc.)
- ✦Engineering productivity analytics dashboard
- ✦Scoped MCP context distribution to multiple AI coding tools
- ✦Design token and component API management
- ✦Collaborative documentation with analytics
- ✦Figma and Storybook data source integration
- ✦Feedback loop for improving AI context quality
- ✦Skill and exporter management for agent capabilities
- ✦BYOK access to 15+ model providers
- ✦Agentic planning-then-build mode
- ✦AI autocomplete
- ✦MCP connections to external systems
- ✦Custom rules and live context tracking
- ✦Local models via Ollama/LM Studio
- ✦VS Code and JetBrains plugins
- ✦AI code completion and snippet generation
- ✦Natural-language code generation
- ✦Code explanation and AI Q&A
- ✦Automated bug detection and fixes
- ✦Zero-config cloud development environment
- ✦Project creation from templates or Git
- →Accelerating software development by automating repetitive tasks
- →Reducing onboarding time for new developers
- →Improving code quality and reducing tech debt
- →Streamlining the app building and deployment process
- →Enhancing developer productivity by keeping them in a state of flow
- →Engineering teams automating ticket-to-PR workflows
- →Developers wanting AI-assisted debugging and test generation
- →Engineering managers tracking AI-driven productivity gains
- →Teams centralizing documentation from scattered sources
- →Product teams giving AI coding agents accurate design-system context
- →Design system managers publishing a single source of truth
- →Engineering teams reducing token usage by scoping agent context per team
- →Code generation, refactoring and debugging
- →Control AI spend with your own keys
- →Switch between frontier models per task
- →Keep code private and data-sovereign
- →Writing and completing code faster with AI
- →Onboarding to unfamiliar codebases
- →Debugging and optimizing code
- →Spinning up dev environments in the browser