The Power of RAG
Retrieval-Augmented Generation (RAG) ensures that your AI responses are grounded in your actual business data, eliminating hallucinations and ensuring accuracy.
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Sub-Second Latency
High-performance vector indices return semantic matches in under 200ms across millions of documents.
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Cited Sources
Every answer includes clickable citations to the source documentation, building trust with users.
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Multilingual Support
Ask questions in any language and get answers derived from documents in any other language.
Every business has documents — PDFs, Word files, Excel spreadsheets, internal wikis. The problem has never been storage; it's finding the right information when you need it. General Bots AI Search solves this by understanding the meaning behind your questions, not just matching keywords.
Sub-Second Semantic Search
Powered by Qdrant vector database and high-fidelity embeddings, AI Search delivers semantic matches in under 200ms across document collections of any size. Unlike keyword search, it understands meaning — finding answers even when users phrase questions differently than the source text.
RAG with Cited Sources
Every answer includes clickable citations pointing to the exact source document and passage. Build trust with users who can verify AI-generated responses against your original content.
Multilingual Document Understanding
Documents in any language can be queried in any other language. The embedding model captures semantic relationships across languages, making your knowledge base accessible to a global audience.
How It Works
Think about the last time you searched for a file on your company's shared drive. You probably knew the answer was in there somewhere, but the search tool couldn't find it because you used different words than the document. AI Search uses vector embeddings to map your documents into a semantic space where meaning drives results, not exact word matches. A search for 'quarterly revenue trends' will find documents about 'financial performance by quarter' because the system understands the concepts are related.
Related Features
Combine AI Search with Talk to Data to query your databases in natural language, or use it alongside Web Automation to index external websites. For training your bot on specific document collections, see Knowledge Training.
Why Use AI Search
High-performance semantic search across millions of documents using Retrieval-Augmented Generation. Vector embeddings, cited sources, multilingual support.
200ms average response time across 10M+ documents. Supports PDF, DOCX, XLSX, PPTX, TXT, HTML, and scanned images via OCR.