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Enterprise data-annotation and evaluation platform pairing a labeling tool with a managed expert annotator workforce.
Google's open-source TypeScript framework for building scalable web apps, featuring signals, reactivity and first-party tooling.
Low-code integration platform for connecting thousands of APIs into workflows and AI agents, including an MCP tool server.
AI prototyping tool that generates UI matching your design system, letting product teams test features fast.
No public pricing
No public pricing
No public pricing
No public pricing
No public pricing
- ✦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)
- ✦Customizable multimodal annotation editors for image, video, text and audio
- ✦Support for RLHF preference data, SFT datasets, RAG and agent evaluation workflows
- ✦Managed expert annotator workforce option
- ✦Data curation, exploration and analytics tools
- ✦Team and project management with SSO on higher tiers
- ✦Integrations with AWS, GCP, Databricks, Snowflake and others
- ✦Signals-based fine-grained reactivity
- ✦Built-in control flow and deferrable views
- ✦Server-side rendering and hydration
- ✦First-party routing, forms and dependency injection
- ✦AI-forward tooling and MCP resources
- ✦In-browser tutorials and playground
- ✦Visual and code-based workflow builder
- ✦Prebuilt AI agent builder and deployment
- ✦Managed authentication across thousands of apps
- ✦MCP server exposing integrations as agent tools
- ✦Scheduled and event-triggered workflows
- ✦Connect SDK for embedding integrations into other products
- ✦AI UI generation from prompts
- ✦Match existing styling and design systems
- ✦Rapid, high-fidelity prototyping
- ✦Live team editing and sharing
- ✦Enterprise security and compliance
- →Creating and manipulating datasets
- →Training small machine learning models
- →Implementing advanced training and inference logic
- →Building applications that require tensor computations
- →Building large-scale labeled datasets to train computer vision or NLP models
- →Running human evaluation and RLHF pipelines for LLM fine-tuning
- →Auditing and scoring AI agent decisions with human review
- →Building scalable single-page apps
- →Enterprise web application development
- →Performance-critical front ends
- →Learning modern web development
- →Building AI agents that call external APIs and tools
- →Automating cross-app workflows such as Slack, Gmail, or Sheets notifications
- →Embedding third-party integrations into a SaaS product
- →Prototyping event-driven automations without heavy infrastructure
- →Prototype new product features
- →Test designs with customers
- →Build design-system-consistent mockups