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AI GitHub companion that summarizes PRs, answers questions and proposes fixes inside issues and pull requests.
One-click bug-reporting tool that auto-captures console, network logs and repro steps for developers.
Google's AI coding assistant for code completion, generation, chat and review across IDEs and GitHub.
AI tool that converts natural-language questions into SQL queries, sold via a Lemon Squeezy storefront with tiered pricing.
Governed data layer connecting marketing, product and finance sources to AI agents for plain-language querying.
No public pricing
Free trial available
No public pricing
Free trial available
- ✦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
- ✦One-click bug capture via browser extension
- ✦Automatic repro steps
- ✦Console, network and device logs
- ✦Instant replay of recent activity
- ✦Backend tracing and an AI debugger
- ✦Integrations with Jira, Linear, GitHub and Slack
- ✦AI code completion and suggestions
- ✦Natural-language code generation
- ✦In-IDE chat assistance
- ✦AI code review
- ✦IDE integrations (VS Code, JetBrains, etc.)
- ✦GitHub integration
- ✦Natural language to SQL query generation
- ✦Standard and Pro subscription tiers
- ✦Checkout and billing via Lemon Squeezy
- ✦Unified connection to 100+ marketing/product/finance data sources
- ✦MCP-compatible interface usable by any AI agent
- ✦Learns custom metric definitions and joins across sources
- ✦Secure credential gateway that keeps raw keys from agents
- ✦Cross-source joins spanning databases, warehouses and product data
- ✦Fine-grained audit logs of every query
- ✦Live dashboards and debugging in plain English
- →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
- →Filing detailed bug reports
- →Reproducing issues faster in QA
- →Sharing debug context with engineers
- →Triaging support bug reports
- →Speeding up coding with AI completions
- →Generating code from plain-language prompts
- →Getting in-editor help and explanations
- →Reviewing pull requests with AI
- →Understanding unfamiliar codebases
- →Generating SQL queries without writing raw syntax
- →Helping non-technical users query databases
- →Speeding up ad hoc data lookups for analysts
- →Marketing teams asking AI agents for campaign or ROAS reports
- →Data teams governing access to metrics across tools
- →Agencies building AI-driven client reporting