Nextwisi Solutions builds Retrieval-Augmented Generation (RAG) platforms and vector search engines that connect internal company PDFs, SQL databases, and CRMs to LLMs with zero hallucination.
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Turn internal company data into an intelligent conversational knowledge engine.
Deploy intelligent Agentic RAG models that reformulate queries, cross-reference multiple documents, and generate precise answers with exact source links.
Optimize vector embeddings and chunking strategies using Pinecone, Qdrant, ChromaDB, and Pgvector for instant query execution.
Combine BM25 keyword matching with dense vector embeddings to ensure technical terms and exact document IDs are retrieved correctly.
Run document extraction and embedding pipelines inside your private cloud environment to maintain strict data privacy.
We solve the LLM hallucination problem by connecting custom vector databases to your enterprise data with real-time semantic retrieval.
Strict source citation architecture that links every AI answer directly to original company documents.
Index millions of internal documents using Pinecone, Qdrant, Milvus, and FAISS vector databases.
Enforce strict document access controls so users only see search results authorized for their role.
Ingest PDFs, Word files, Notion pages, SQL tables, and SharePoint drives automatically.
Everything you need to know about our AI agent development, security compliance, pricing, and project execution timelines.
RAG is an AI technique that retrieves relevant information from your private company files before generating a response, ensuring 100% accurate, zero-hallucination answers.
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