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Data-annotation platform with AI-assisted labeling tools and team workflows for building ML training datasets.
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.
Documentation platform for publishing accurate, AI-ready docs sites, with Git sync and an MCP server for AI tools.
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
No public pricing
Free trial available
Free trial available
- ✦AI-assisted data annotation tools
- ✦Training-data platform (BasicAI Cloud)
- ✦Team and project management
- ✦Annotation services
- ✦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
- ✦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
- ✦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
- →Labeling images and data for ML models
- →Managing annotation teams and projects
- →Producing training datasets at scale
- →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
- →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
- →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
