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No-code builder for custom ChatGPT-style website chatbots trained on your data for support, lead gen and engagement.
Free all-in-one desktop AI app to chat with your documents and run RAG and AI agents fully local and private.
AI coding platform and IDE that orchestrates multiple agent sessions and lets teams plug in their own AI subscriptions.
Prompt engineering, management, and LLM observability platform.
Infrastructure company building large-scale GPU data centers and compute for AI, including Anthropic's compute buildout.
Free trial available
No public pricing
No public pricing
No public pricing
No public pricing
- ✦No-code chatbot creation
- ✦Train on files/URLs/Notion/Zendesk
- ✦Website embed widget
- ✦Conversation analytics
- ✦~95 language support
- ✦Custom branding
- ✦Chat with your documents (RAG)
- ✦Runs locally and offline for privacy
- ✦Supports any LLM (local or cloud)
- ✦Built-in AI agents
- ✦Handles PDFs, Word, CSV, codebases
- ✦No-code setup
- ✦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
- ✦Prompt management
- ✦Prompt evaluations
- ✦LLM observability
- ✦Team collaboration
- ✦Version control for prompts
- ✦A/B testing of prompts
- ✦Prompt Registry
- ✦Historical backtests
- ✦Regression tests
- ✦Usage monitoring
- ✦Large-scale GPU and data-center infrastructure for AI
- ✦Power acquisition and data-center design/build
- ✦Fast deployment (gigawatts in ~6 months)
- ✦Operates both hardware and software stack
- →Website customer support
- →Lead generation
- →FAQ and audience engagement
- →Privately querying your own documents
- →Running local AI without the cloud
- →Building AI agents over your data
- →Using multiple LLM providers in one app
- →Shipping code faster with AI agents
- →Coordinating agent work across a team
- →Managing tasks and artifacts in one place
- →Running many parallel agent sessions
- →Scaling customer support automation with LLMs
- →Empowering non-technical teams with prompt engineering
- →Building personalized AI interactions
- →Debugging LLM agents
- →Improving content creation processes
- →Managing and monitoring prompts with a team
- →Training and running large AI models at scale
- →Provisioning GPU compute for AI labs
- →Building dedicated AI data-center capacity