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All-in-one no-code platform combining forms, workflow automation, AI agents, a database and email in a single subscription.
Documentation platform for publishing accurate, AI-ready docs sites, with Git sync and an MCP server for AI tools.
GitHub-based engineering analytics that tracks contributions, automates performance reviews and adds gamification for dev teams.
AI bot that reviews GitHub pull requests, flagging bugs, security and performance issues with detailed, consistent feedback.
No public pricing
No public pricing
Free trial available
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)
- ✦Publish structured documentation sites
- ✦Git sync for docs-as-code workflows
- ✦AI setup agent to build and import docs
- ✦GitBook MCP server for AI access
- ✦Enterprise controls
- ✦Free tier to start
- ✦Contribution and work-quality analytics
- ✦Automated, AI-powered performance reviews
- ✦Retrospective insights
- ✦Operational bottleneck alerts
- ✦Gamification with XP, levels and leaderboards
- ✦Uses Git metadata without accessing source code
- ✦Automated AI reviews on GitHub PRs
- ✦Bug, security and performance detection
- ✦Detailed, consistent feedback
- ✦Interactive code-review tool for snippets
- ✦Multi-language explanations
- ✦Customizable review rules (Pro)
- ✦Self-host/custom LLM (Enterprise)
- →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
- →Publish product and API documentation
- →Maintain docs-as-code with Git sync
- →Make docs consumable by AI assistants
- →Import existing docs into a hosted site
- →Automating developer performance reviews
- →Spotting delivery bottlenecks
- →Generating retrospective insights
- →Motivating teams via gamification
- →Automate pull-request reviews
- →Catch issues before merge
- →Get plain-English code explanations
- →Keep review quality consistent