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Documentation platform for publishing accurate, AI-ready docs sites, with Git sync and an MCP server for AI tools.
AI agent-based end-to-end testing platform for SaaS teams that runs exploratory and PR-triggered tests without maintaining test scripts.
Online database-design tool with sample schemas and an AI generator to explore, modify or build database structures visually.
AI chat assistant for analysts that generates and fixes SQL using an organization's own saved query history.
No public pricing
Free trial available
No public pricing
No public pricing
No public pricing
- ✦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
- ✦AI agents that visually explore and test UI like a real user
- ✦Automatic PR-triggered test runs via GitHub/Vercel preview integration
- ✦Self-healing tests that adapt to UI and workflow changes
- ✦Mobile web, iOS, and Android app testing support
- ✦Detailed debugging with screenshots, logs, and failure reasoning
- ✦Cloud-native execution with no source-code access required
- ✦Library of sample database designs
- ✦Visual database designer / diagram tool
- ✦AI database generator
- ✦Modify and optimize existing schemas
- ✦SQL script export
- ✦Dialect converters (MySQL/PostgreSQL/MSSQL)
- ✦AI chat trained on the user's own SQL repository
- ✦query saving, tagging, and versioning
- ✦team folder permissions and sharing
- ✦Chrome extension and IntelliJ plugin
- ✦SOC2-compliant security with no database access needed
- ✦table and field lookup assistance
- →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
- →Engineering teams wanting regression testing without maintaining scripts
- →SaaS companies needing continuous QA feedback on every pull request
- →Teams replacing manual QA hours with automated agent-driven testing
- →Finding a starting schema for a project
- →Designing a database visually
- →Generating a schema with AI
- →Converting between SQL dialects
- →sharing reusable SQL snippets across an analytics team
- →quickly fixing syntax errors in existing queries
- →onboarding new analysts to a team's existing SQL logic
- →building a searchable personal or team SQL knowledge base