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AI knowledge base that saves, summarizes and connects articles, videos, podcasts and PDFs into a graph you can chat with.
Developer API suite (Reader, Embeddings, Reranker) that turns web content into LLM-ready data for search and RAG.
Infrastructure company building large-scale GPU data centers and compute for AI, including Anthropic's compute buildout.
AI text detector and humanizer that scores AI-written content and rewrites it in one click to read naturally.
Pay-as-you-go API aggregating thousands of image, video, audio and LLM models with custom inference hardware for lower per-request cost.
No public pricing
No public pricing
Free trial available
Free trial available
- ✦One-click saving of articles, videos, podcasts, PDFs
- ✦AI summaries of saved content
- ✦Automatic tagging and knowledge-graph linking
- ✦Chat with your knowledge using GPT, Claude or Gemini
- ✦Spaced-repetition quizzes
- ✦Browser extension, web and mobile apps; API/MCP access
- ✦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
- ✦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 probability scoring with sentence-level highlighting
- ✦One-click humanization/rewrite of flagged text
- ✦Detection across multiple major AI models
- ✦Saved history of checks and rewrites
- ✦Free unlimited detection with limited humanization words
- ✦Single API for image, video, audio, 3D and LLM models
- ✦Standardized model addressing across hosted, partner and custom uploads
- ✦Support for LoRAs, ControlNets, VAEs and embeddings on open-source models
- ✦WebSocket and REST access with async webhook delivery
- ✦Pay-per-request billing with no infrastructure to manage
- ✦Raw serverless GPU/CPU compute for custom workloads
- →Building a personal 'second brain'
- →Summarizing long content to save time
- →Chatting with your own saved knowledge
- →Retaining what you read via spaced repetition
- →Ground LLMs with clean web content
- →Build semantic and RAG search
- →Rerank retrieved results
- →Give AI agents live web access
- →Training and running large AI models at scale
- →Provisioning GPU compute for AI labs
- →Building dedicated AI data-center capacity
- →Students checking AI-assisted drafts before submission
- →Writers and bloggers removing robotic AI phrasing before publishing
- →Professionals sending AI-drafted emails or copy that read naturally
- →Adding AI image or video generation to an app without managing infra
- →Batching multi-modal generation tasks in one API call
- →Running custom fine-tuned models via Model Upload
- →Cutting inference costs at high generation volume