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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.
Managed AI ranking engine powering personalized search, recommendations, and feeds via a SQL-like query language.
End-to-end computer vision platform for teams annotating data, training YOLO models, and deploying them at scale.
Open-source React/Angular SDK and platform for embedding agentic, generative-UI copilots into apps, Slack and Teams.
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Free trial available
- ✦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
- ✦ShapedQL SQL-style query interface for retrieval and ranking
- ✦Hybrid semantic and keyword search
- ✦Continuous learning from user feedback signals
- ✦30+ native data connectors for warehouses and streams
- ✦Sub-50ms query latency
- ✦Python and TypeScript SDKs plus MCP support
- ✦Smart data annotation with SAM-powered one-click masks across six task types
- ✦Cloud training with 22+ GPU configurations from RTX 2000 Ada to B200
- ✦Support for YOLOv5 through YOLO26 model families
- ✦One-click deployment across 43 global regions with auto-scaling
- ✦Export to 18 formats including ONNX, TensorRT, and CoreML
- ✦Live training metrics and experiment comparison dashboard
- ✦React and Angular frontend SDKs
- ✦Agent-rendered generative UI
- ✦AG-UI agent-user interaction protocol
- ✦Connectors for LangChain and other frameworks
- ✦Pre-built customizable chat/sidebar components
- ✦Slack and Teams integrations
- ✦Thread and state persistence
- →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
- →Personalizing 'for you' content feeds
- →Building product recommendation systems
- →Powering RAG retrieval with behavioral ranking
- →Adding hybrid search to an e-commerce site
- →Building and training custom object detection or segmentation models
- →Labeling large image/video datasets for computer vision projects
- →Deploying vision models to edge or mobile devices
- →Running quality control or defect detection in manufacturing
- →Powering retail, logistics, or agriculture vision applications
- →Adding an AI assistant to a SaaS product
- →Building agents that render interactive UI
- →Deploying copilots across Slack and Teams
- →Connecting existing agents to a frontend