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Free all-in-one desktop AI app to chat with your documents and run RAG and AI agents fully local and private.
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.
No public pricing
No public pricing
Free trial available
- ✦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
- ✦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
- →Privately querying your own documents
- →Running local AI without the cloud
- →Building AI agents over your data
- →Using multiple LLM providers in one app
- →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