KnowSilos is a powerful decision-making tool designed for businesses of all sizes. It automates the gathering of siloed, unstructured information, scrapes real-time data from the internet, and leverages decision-making frameworks to provide actionable insights. With features like advanced knowledge graphs and real-time analysis, KnowSilos helps organizations stay ahead of market trends, ensure data credibility, and enhance collaborative decision-making. The platform also addresses critical challenges like information overload and ineffective knowledge sharing.

TiDB is an integrated database solution tailored for AI applications. It supports vector search, semantic knowledge graph search, and operational data management. Its serverless architecture ensures reliability and scalability, eliminating the need for manual data synchronization and management of multiple data stores. With enterprise-grade features such as role-based access control, encryption, and high availability, TiDB is ideal for production-ready AI applications that demand performance, security, and ease of use. TiDB's platform compatibility spans both cloud-based and local deployments, making it versatile for various infrastructure needs.

Chat2Graph integrates with the TuGraph graph database to deliver a conversational interface for graph data exploration. Through pre-built connectors and a prompt-engineering layer, it translates user intents into valid graph queries, handles schema discovery, suggests optimizations, and executes queries in real time. Results can be rendered as tables, JSON, or network visualizations via a web UI. Developers can customize prompt templates, integrate custom plugins, or embed Chat2Graph in Python applications. It's ideal for rapid prototyping of graph-powered applications and enables domain experts to analyze relationships in social networks, recommendation systems, and knowledge graphs without writing manual Cypher syntax.

Research Navigator is an AI-driven tool that automates literature review tasks for researchers, students, and professionals. Leveraging advanced NLP and knowledge graph technologies, it retrieves and filters relevant scientific articles based on user-defined queries. It extracts salient points, methodologies, and results to generate concise summaries, highlights differences across studies, and provides side-by-side comparisons. The platform supports citation export in multiple formats and integrates with existing documentation workflows via API or CLI. With customizable search parameters, users can focus on specific domains, publication years, or keywords. The agent also maintains session-based memory, enabling follow-up queries and incremental refinement of research topics.

Arsenal by CluSTR transforms how teams manage and interact with their knowledge by using advanced AI Agents. It processes multiple file types (PDF, Word, text, images) into vector embeddings, builds searchable knowledge graphs, and delivers real-time conversational interfaces. Users can create custom agents for tasks like research assistance, code review, and report generation. With seamless integrations (Google Drive, Slack, GitHub), role-based access, and API endpoints, Arsenal streamlines workflows and empowers users to extract insights faster.

DeepResearch leverages advanced natural language processing to crawl multiple academic sources, extract key findings, and produce structured summaries. It creates interactive knowledge graphs, highlights relevant citations, and generates insights on demand. Users can tag, organize, and export results to citation managers or collaborative platforms. The AI continuously learns user preferences to improve relevance and depth over time, making literature reviews faster and more comprehensive.

Graphium is a knowledge graph and LLM orchestration framework that supports ingestion of structured data, creation of semantic embeddings, and hybrid retrieval for Q&A and chat. It integrates with popular LLMs, graph databases, and vector stores to enable explainable, graph-powered AI agents. Users can visualize graph structures, query relationships, and employ multi-hop reasoning. It provides RESTful APIs, SDKs, and a web UI for managing pipelines, monitoring queries, and customizing prompts, making it ideal for enterprise knowledge management and research applications.

LangGraph-Chatchat functions as a memory management plugin for the ChatChat conversational framework, utilizing LangGraph’s graph database model to store and retrieve conversation context. During runtime, user inputs and agent responses are converted into semantic nodes with relationships, forming a comprehensive knowledge graph. This structure allows efficient querying of past interactions based on similarity metrics, keywords, or custom filters. The plugin supports configuration of memory persistence, node merging, and TTL policies, ensuring relevant context retention without bloat. With built-in serializers and adapters, LangGraph-Chatchat seamlessly integrates into ChatChat deployments, providing developers a robust solution for building AI agents capable of maintaining long-term memory, improving response relevance, and handling complex dialog flows.