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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.

Milvus is a cloud-native vector database designed to handle large-scale unstructured data such as text, images, and multimodal content. Its distributed architecture separates compute from storage, enabling horizontal scaling and high availability. Milvus supports multiple vector indexing methods, hardware acceleration, and advanced retrieval capabilities, including Approximate Nearest Neighbor (ANN), metadata filtering, and dense-sparse hybrid search. It is widely used in AI applications such as semantic search, recommendation systems, and Retrieval-Augmented Generation (RAG). Additionally, Milvus offers comprehensive data security features, including authentication, encryption, and fine-grained access control.

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.

Vetted AI is an intelligent shopping assistant designed to simplify and enhance the online shopping experience. Leveraging artificial intelligence, it collects and analyzes product reviews, expert opinions, and user feedback from trusted sources such as Reddit and YouTube. By consolidating this information, Vetted AI delivers personalized product recommendations, price comparisons, and concise summaries of pros and cons, enabling users to make informed purchasing decisions quickly and confidently—without the need to search across multiple websites.

Lianqi AI is a professional business search platform that leverages a large language model with billions of parameters to deliver comprehensive enterprise intelligence services. The platform aggregates over 180 million real-time bidding and procurement records, combined with extensive business knowledge, to provide precise commercial insights. Users can access company information and industry analysis through natural language queries, and identify potential business opportunities using the platform's advanced data processing and summarization capabilities.

Jina AI is a versatile, modular platform designed to build high-performance, scalable search systems by leveraging deep learning and neural networks. It supports multimodal data including text, images, and videos, and provides a comprehensive ecosystem for developing custom search pipelines, integrating cutting-edge models such as CLIP, embeddings, rerankers, and small language models. Jina's architecture enables distributed computing and real-time search, with containerization for seamless deployment, making it ideal for enterprise, e-commerce, content management, and AI-driven applications.