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Full-stack platform for web scraping, data extraction, and automation; category leader.
AI SQL toolkit for analysts and developers to generate, optimize, validate, format and explain queries across 30+ database engines.
AI GitHub companion that summarizes PRs, answers questions and proposes fixes inside issues and pull requests.
Turns Git commits and PRs into AI-summarized daily or weekly reports delivered to Slack or email, no source access.
Open-source and cloud SQL agent that lets non-technical users query company databases in natural language, with admin controls for teams.
Free trial available
Free trial available
No public pricing
No public pricing
Free trial available
- ✦Web scraping
- ✦Data extraction
- ✦Browser automation
- ✦AI agents
- ✦Anti-blocking
- ✦Proxy rotation
- ✦Open-source tools (Crawlee)
- ✦Ready-made tools and code templates
- ✦Natural-language to SQL/NoSQL query generation
- ✦AI-driven query optimization with rewrite suggestions
- ✦Syntax validation with automated error fixes
- ✦Query formatting and cross-engine conversion
- ✦Schema-aware data source connections with autosuggest
- ✦Rule-based guardrails per connected data source
- ✦Support for large schemas with 900+ tables
- ✦Chat inside GitHub issues and PRs
- ✦Task-to-implementation plans with code
- ✦Automatic bug-fix suggestions
- ✦Pull-request summaries for faster review
- ✦Full-codebase context
- ✦GitHub-native integration
- ✦AI-summarized commit and PR reports
- ✦Daily and weekly scheduled digests
- ✦Slack and email delivery
- ✦One-click OAuth or webhook setup
- ✦GitHub, GitLab and Bitbucket support
- ✦Templates for standups and reports
- ✦Natural-language to SQL query generation
- ✦Support for multiple LLM providers and database backends
- ✦Multi-turn, multi-database conversational querying
- ✦Role-based access control on hosted tiers
- ✦Real-time observability and tracing
- ✦Hosted vector database for agent memory
- ✦Audit logging and long-term data retention
- →Data for generative AI
- →Lead generation
- →Market research
- →Sentiment analysis
- →Analysts writing SQL without deep query-syntax knowledge
- →Developers debugging and optimizing slow queries
- →Teams standardizing SQL formatting across a codebase
- →Migrating queries between database engines
- →Learners wanting plain-language explanations of SQL statements
- →Speeding up pull-request reviews
- →Implementing features from task descriptions
- →Debugging with AI-proposed solutions
- →Answering questions about a repo
- →Boosting a solo developer's output
- →Keep stakeholders updated on what shipped
- →Replace manual status updates and standups
- →Give teams visibility into Git activity
- →Letting non-SQL business users query company data directly
- →Reducing analyst time spent writing routine SQL
- →Deploying a governed, access-controlled chat-to-SQL agent for a team
- →Self-hosting an open-source text-to-SQL agent for full control