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All-in-one no-code platform combining forms, workflow automation, AI agents, a database and email in a single subscription.
Free tool that auto-generates conversational, browsable documentation for any public GitHub repo, from the makers of Devin.
Online database-design tool with sample schemas and an AI generator to explore, modify or build database structures visually.
AI SQL query optimization tool for developers to detect performance bottlenecks and get explainable tuning recommendations.
No public pricing
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
- ✦AI-generated documentation for GitHub repos
- ✦Conversational Q&A about a codebase
- ✦Browsable index of popular repositories
- ✦Deep code indexing via Devin
- ✦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)
- ✦Zero-configuration SQL optimization across multiple database engines
- ✦AI-powered query rewriting engine
- ✦Bottleneck detection with smart index recommendations
- ✦Dual-pane SQL diff viewer for before/after comparison
- ✦AI query plan explainer with step-by-step reasoning
- ✦MyBatis XML auto-rewrite support
- →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
- →Understanding an unfamiliar codebase quickly
- →Onboarding to open-source projects
- →Answering questions about repo internals
- →Finding a starting schema for a project
- →Designing a database visually
- →Generating a schema with AI
- →Converting between SQL dialects
- →Backend developers speeding up slow production queries
- →Teams reducing manual SQL tuning workload
- →Engineers wanting explainable reasoning behind optimization suggestions
- →Companies standardizing query performance across MySQL/PostgreSQL systems