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  • Inquir is an AI-powered search platform for creating custom search engines and integrating diverse data sources.
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    What is Inquir?
    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.
    Inquir Core Features
    • Custom search engines
    • AI-powered RAG chatbots
    • Enterprise search solutions
    • Research and analytics platforms
    • E-commerce search optimization
    • Context-aware search
  • Transform your business insights with Stackpointer's advanced search engine.
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    What is Stackpointer?
    Stackpointer serves as a powerful business search engine that allows users to dive deep into technology stacks and company information. By leveraging advanced AI algorithms, it analyzes data to provide users with comprehensive insights about various organizations. Users can search through a wide array of parameters, identify potential opportunities, and assess competitive landscapes effectively. This tool is especially beneficial for business owners, researchers, or anyone looking to gain a better understanding of the ever-evolving market dynamics.
  • Advanced Retrieval-Augmented Generation (RAG) pipeline integrates customizable vector stores, LLMs, and data connectors to deliver precise QA over domain-specific content.
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    What is Advanced RAG?
    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.
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