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Developer API that gives AI agents persistent memory, retrieval, and connectors, usable both as infrastructure and a personal app.
Infrastructure company building large-scale GPU data centers and compute for AI, including Anthropic's compute buildout.
Enterprise voice-AI platform for building phone agents on dedicated infrastructure, with per-minute pricing and HIPAA/SOC2/PCI compliance.
Platform for building and interacting with AI-powered virtual beings.
Developer API suite (Reader, Embeddings, Reranker) that turns web content into LLM-ready data for search and RAG.
No public pricing
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No public pricing
No public pricing
- ✦Persistent, structured memory built as a knowledge graph
- ✦Sub-300ms hybrid retrieval (RAG) with reranking
- ✦Native filesystem mount for agent memory access
- ✦Connectors to Slack, Notion, Drive, Gmail, GitHub, S3
- ✦Automatic extraction from PDFs, images, and audio
- ✦User profile and behavior tracking across sessions
- ✦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
- ✦AI phone agents (inbound and outbound)
- ✦No-code agent builder (Norm)
- ✦Scenario testing before launch
- ✦Omnichannel voice, SMS, iMessage and chat
- ✦Telephony and CRM integrations
- ✦Enterprise compliance and on-prem options
- ✦AI chatbot creation with unique personalities and voices
- ✦Decentralized platform for AI-native apps
- ✦Voice and video conversations with AI beings
- ✦AI agent building and sharing
- ✦Creator economy for AI apps
- ✦Reader API converts URLs to Markdown
- ✦Multimodal multilingual embedding models
- ✦Reranker for stronger search relevance
- ✦Web search endpoint returning SERP data
- ✦MCP server for use inside LLMs
- ✦Native inference inside Elasticsearch
- →Developers adding long-term memory to AI agents
- →Teams building agents that need to sync with existing tools
- →Individuals wanting one memory layer shared across multiple AI assistants
- →Training and running large AI models at scale
- →Provisioning GPU compute for AI labs
- →Building dedicated AI data-center capacity
- →Automating customer-service calls
- →IVR replacement and appointment booking
- →Outbound sales and lead qualification
- →Regulated-industry voice automation
- →Interacting with AI friends like Shizuku for voice and video conversations
- →Creating AI-native apps using generative AI models
- →Building and sharing AI agents within the MyShell ecosystem
- →Ground LLMs with clean web content
- →Build semantic and RAG search
- →Rerank retrieved results
- →Give AI agents live web access