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Managed service that finds and removes personal data from broker sites and public records, aimed at executives, families and firms.
Developer API that gives AI agents persistent memory, retrieval, and connectors, usable both as infrastructure and a personal app.
High-performance open-source vector database for production AI retrieval and RAG, for teams needing scale, hybrid search, or self-hosting.
Serverless platform for running and fine-tuning image, video, audio and 3D generative models via one fast API.
Well-known chat-with-PDF tool for summarizing and extracting; strong traffic.
No public pricing
No public pricing
- ✦Personal data discovery across broker sites
- ✦Ongoing removal and re-exposure prevention
- ✦Continuous digital-footprint monitoring
- ✦Organization and executive protection plans
- ✦Included identity-theft insurance
- ✦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
- ✦Hybrid dense and sparse vector search (BM25, SPLADE, miniCOIL)
- ✦Advanced metadata filtering applied during search traversal
- ✦Multivector support for multimodal retrieval
- ✦Reranking with score boosting and late-interaction models (ColBERT, MMR)
- ✦Flexible deployment: cloud, hybrid, private, or edge
- ✦Rust-based engine optimized for low-latency, high-scale search
- ✦1,000+ generative model APIs
- ✦Serverless GPU inference engine
- ✦On-demand and dedicated GPU clusters
- ✦Model fine-tuning and custom deployments
- ✦Bring-your-own-weights and private endpoints
- ✦SOC 2 compliance and enterprise features
- ✦Chat with PDF documents
- ✦Summarize PDF content
- ✦Extract information from PDFs
- ✦Source citation for answers
- ✦OCR support
- ✦AI Agents for document analysis
- ✦Capture & Ask feature
- ✦Chatbot widget (add-on)
- →Removing personal info from the internet
- →Protecting executives from targeted attacks
- →Reducing family privacy exposure
- →Ongoing identity monitoring
- →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
- →Building retrieval-augmented generation (RAG) pipelines
- →Powering AI recommendation and semantic search systems
- →Enterprises needing on-prem or hybrid deployment for compliance
- →AI agent platforms needing fast contextual retrieval at scale
- →Adding image/video generation to an app
- →Running fast diffusion-model inference at scale
- →Training or fine-tuning custom generative models
- →Analyzing legal documents
- →Summarizing financial reports
- →Extracting key information from books
- →Reviewing scientific papers
- →Understanding user manuals
- →Creating employee training materials