vectorize

Defense Situational Awareness Platform integrating multi-domain sensor data for threat detection and tracking

An AI-powered document and knowledge management platform that integrates WeChat Official Accounts and web content, supporting local file processing and personal knowledge storage.

A reverse image search platform that finds similar images, sources, and online usage locations by uploading images.

A research citation analysis platform that identifies supporting, refuting, or merely mentioning relationships between citations and papers.

Cloud-native vector database supporting approximate nearest neighbor search and metadata filtering for massive unstructured data.

Cloud search platform delivering real-time, multilingual, and customizable search results and analytics tools

Superlinked is a sophisticated vector embedding platform designed for AI engineers to build advanced search, recommendation, and analytics applications. It supports combining structured and unstructured data into multimodal vectors to capture rich contextual information. The framework offers fine-grained control over retrieval objectives such as relevance, freshness, and popularity, enabling more accurate and personalized results. Superlinked provides a Python SDK and a self-hostable REST API server that seamlessly integrate with vector databases and backend services, simplifying deployment from prototype to production.

Kagi is a subscription-based search engine dedicated to delivering fast, relevant, and ad-free search results while prioritizing user privacy. It aggregates data from multiple sources, including its own web crawler, and offers unique features such as customizable search lenses, domain boosting and blocking, and in-depth site insights. Kagi integrates a powerful AI Assistant that combines mainstream large language models with real-time web search to provide enhanced support for creativity, research, and programming. The platform emphasizes user control over the search experience and data privacy, rejecting ads and tracking in favor of a transparent subscription model.

Meet New Books is a user-centric book discovery platform designed to simplify the process of finding your next great read. By combining your past favorite books, browsing behavior, and community preferences, the platform delivers personalized book recommendations without relying on traditional star ratings. Easily discover new books by title, genre, themes, and keywords, enjoying a dynamic and interest-aligned reading experience. Additionally, the platform connects like-minded readers through community features and allows users to create personal bookshelves for organized management of recommended reading lists.

Qdrant is a cutting-edge vector search engine and database optimized for storing, retrieving, and managing high-dimensional vectors along with their metadata payloads. It excels in use cases such as similarity search, recommendation systems, RAG, anomaly detection, and advanced semantic search. Built with Rust for high performance and reliability, Qdrant supports multiple deployment options including local Docker, hybrid cloud, and fully managed cloud services. With advanced indexing, quantization, and filtering capabilities, Qdrant delivers efficient, scalable, and precise vector search for modern AI workloads.

TheGigaBrain is an advanced AI-powered search platform that aggregates and analyzes discussions from online communities such as Reddit and YouTube. Leveraging large language models, the platform identifies the most valuable comments and posts, delivering concise summaries and source citations to users. Designed to help users quickly retrieve knowledge on a wide range of topics, it combines human feedback with cutting-edge AI technology to provide accurate and trustworthy insights.

Zilliz Cloud is a cloud-native vector database platform designed to efficiently store, index, and retrieve massive vector embeddings for AI-powered applications. Built on the open-source Milvus engine, the platform delivers enterprise-grade scalability, security, and performance optimization through AI-powered indexing and query optimization. It supports multi-cloud deployment and flexible computing resources, seamlessly integrates with mainstream AI models and frameworks, and enables a wide range of use cases such as semantic search, recommendation systems, and anomaly detection.