Credal is a robust platform designed for creating secure AI applications. It provides essential tools such as workflow assistants, enterprise search, and AI chat functionality. All data interactions are governed by strict security measures, access controls, and compliance protocols, ensuring that sensitive information is protected. Whether you are a developer looking to embed generative AI experiences into your applications or a business seeking to leverage AI capabilities securely, Credal offers a versatile solution tailored to meet various needs.

Inquir is the ultimate tool for creating personalized search engines tailored to your data. With Inquir, you can build custom search solutions, integrate various data sources, and create advanced AI-driven retrieval systems. It provides tools like AI-powered RAG chatbots, enterprise search solutions, and research and analytics platforms. Inquir helps you transform user experiences with context-aware search functionalities. It's ideal for enhancing product discovery in e-commerce, boosting research workflows, and improving enterprise search capabilities.

Jina AI is a leading provider of cloud-native neural search solutions. Their open-source framework leverages state-of-the-art deep learning to enable businesses and developers to efficiently handle and search through diverse data types. This approach facilitates seamless deployment, scaling, and orchestration of search systems, making it ideal for enterprises looking to improve information retrieval and data management capabilities.

SearchUnify offers a comprehensive unified cognitive platform designed to elevate customer support and self-service through AI-powered solutions. It provides enterprise search functionalities, insights engines, and various AI applications to improve information retrieval, issue resolution, and customer engagement. By leveraging multi-platform search, natural language processing, and content gap analysis, SearchUnify facilitates faster, more accurate responses and boosts overall customer satisfaction.

At its core, Advanced RAG provides developers with a modular architecture to implement RAG workflows. The framework features pluggable components for document ingestion, chunking strategies, embedding generation, vector store persistence, and LLM invocation. This modularity allows users to mix-and-match embedding backends (OpenAI, HuggingFace, etc.) and vector databases (FAISS, Pinecone, Milvus). Advanced RAG also includes batching utilities, caching layers, and evaluation scripts for precision/recall metrics. By abstracting common RAG patterns, it reduces boilerplate code and accelerates experimentation, making it ideal for knowledge-based chatbots, enterprise search, and dynamic content summarization over large document corpora.