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Enterprise AI coding assistant that pulls context from an entire codebase to power chat, code edits and debugging.
AI coding platform and IDE that orchestrates multiple agent sessions and lets teams plug in their own AI subscriptions.
One-click bug-reporting tool that auto-captures console, network logs and repro steps for developers.
AI agent-based end-to-end testing platform for SaaS teams that runs exploratory and PR-triggered tests without maintaining test scripts.
Test management platform unifying manual and automated test results with AI-assisted case generation, for scaling QA teams.
No public pricing
Free trial available
No public pricing
Free trial available
- ✦Codebase-aware developer chat
- ✦AI code completions and inline edits
- ✦Customizable and shareable prompts
- ✦Automatic bug identification and debugging help
- ✦Context filters to exclude sensitive repos
- ✦Integrates with major code hosts and IDEs
- ✦AI coding IDE with agent orchestration
- ✦Run and manage multiple agent sessions
- ✦Task, artifact and collaboration tools
- ✦Bring-your-own AI subscription or API keys
- ✦Cloud-scale agent execution
- ✦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 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
- ✦Central test case repository with reporting dashboards
- ✦AI conversion of manual test cases into automated test scripts
- ✦CI/CD-connected automated test orchestration
- ✦Requirements-to-test traceability reporting
- ✦MCP server for connecting AI agents to test data
- ✦20+ integrations including Jira, GitHub, and Slack
- →Engineers asking questions about an unfamiliar large codebase
- →Teams standardizing common coding tasks with shared prompts
- →Developers debugging errors faster with AI-assisted context
- →Enterprises running large-scale code migrations
- →Shipping code faster with AI agents
- →Coordinating agent work across a team
- →Managing tasks and artifacts in one place
- →Running many parallel agent sessions
- →Filing detailed bug reports
- →Reproducing issues faster in QA
- →Sharing debug context with engineers
- →Triaging support bug reports
- →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
- →QA teams consolidating scattered CI, manual, and automated results
- →Engineering orgs converting manual test backlogs into automation
- →Enterprises needing audit-ready traceability for regulated software
