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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.
One-click bug-reporting tool that auto-captures console, network logs and repro steps for developers.
Data-labeling and RL data platform supplying training data, environments and evaluation for frontier AI labs and enterprises.
No public pricing
No public pricing
No public pricing
Free trial available
No public pricing
- ✦CSV to API conversion
- ✦Data parsing (CSV to JSON)
- ✦Filtering capabilities
- ✦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
- ✦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
- ✦Data labeling across modalities
- ✦RL environments and reward signals
- ✦Custom model evaluations and benchmarks
- ✦Human preference/annotation from an expert network
- ✦Recursion RL platform for enterprise agents
- ✦Robotics data (video, trajectories)
- →Sharing CSV data with a team via an API
- →Creating a public API from CSV data
- →Filtering and accessing specific data within a CSV file programmatically
- →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
- →Filing detailed bug reports
- →Reproducing issues faster in QA
- →Sharing debug context with engineers
- →Triaging support bug reports
- →Building training and evaluation datasets
- →Post-training and RLHF for models
- →Benchmarking model capability
- →Training enterprise specialist agents