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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.
Developer framework and LlamaParse service for parsing documents and building AI agents and RAG workflows over them.
Vertical AI platform delivering industry-specific predictive, generative and agentic applications for retail, finance and industrial firms.
No public pricing
No public pricing
No public pricing
No public pricing
- ✦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
- ✦LlamaParse document parsing and extraction
- ✦Open-source framework for AI agents and workflows
- ✦Document indexing for retrieval/RAG
- ✦Prebuilt solutions by industry and use case
- ✦Free starter credits for LlamaParse
- ✦Industry-specific AI applications (retail, finance, industrial, IT, media)
- ✦Eureka platform for making enterprise data AI-ready
- ✦Agentic AI agents for tasks like SAR drafting or inventory routing
- ✦Financial-crime alert reduction and investigation tools
- ✦Demand forecasting and shelf/pricing optimization
- ✦Real-time industrial root-cause analysis
- →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
- →Parse complex documents for AI apps
- →Build RAG and agent workflows
- →Automate invoice and claims processing
- →Search across technical documents
- →Retailers optimizing pricing, inventory and promotions
- →Banks reducing false-positive fraud and AML alerts
- →Manufacturers diagnosing production downtime in real time
- →Enterprises building custom AI agents on their own data