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Testing and evaluation platform for voice and chat AI agents, running simulations, production monitoring and human QA review.
AI agent-based end-to-end testing platform for SaaS teams that runs exploratory and PR-triggered tests without maintaining test scripts.
AI prototyping tool that generates UI matching your design system, letting product teams test features fast.
AI copilot that turns text or references into editable, multi-screen UI prototypes exportable to Figma or code.
No public pricing
Free trial available
No public pricing
No public pricing
No public pricing
Free trial available
- ✦Fast tensor operations
- ✦Differentiable tensors for gradient-based optimization
- ✦Network connectivity
- ✦Integration with Bun and Flashlight
- ✦Support for GPU computation with CUDA (Linux) and CPU computation (macOS)
- ✦Voice agent simulation at scale
- ✦Production call monitoring and evals
- ✦Human QA review queues
- ✦Regression testing across changes
- ✦Vendor bake-off comparisons
- ✦API, CLI and MCP interfaces
- ✦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
- ✦AI UI generation from prompts
- ✦Match existing styling and design systems
- ✦Rapid, high-fidelity prototyping
- ✦Live team editing and sharing
- ✦Enterprise security and compliance
- ✦Text-to-UI prototype generation
- ✦Design from image or Figma references
- ✦Interactive multi-screen prototypes
- ✦Conversational AI editing
- ✦Export to Figma, HTML/CSS, images
- ✦MCP access for coding agents
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Pressure-testing voice agents before launch
- →Catching quality drift in production
- →Comparing voice AI vendors objectively
- →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
- →Prototype new product features
- →Test designs with customers
- →Build design-system-consistent mockups
- →Rapid UI wireframing
- →Prototyping product screens
- →Recreating a reference UI
- →Handing designs to developers