Keyword Focus: RAG Chatbot

RAG & Knowledge Base AI Chatbots

Build a custom **knowledge base chatbot** using secure vector database retrievals (RAG). Keep responses accurate without hallucinations.

Why Retrieval-Augmented Generation?

Passing too much text into an LLM context is expensive and error-prone. RAG isolates only the most relevant text fragments from your vectors and uses them to guide the answer, ensuring high compliance and trust.

RAG Infrastructure

Precise vector chunking
Multi-document knowledge indexing
Semantic text matching lookup
Hallucination guardrail filters

Build a RAG Chatbot

Product FAQ

Frequently Asked Questions

Everything you need to know about Askora's training, pricing, technology, and compliance.

Askora crawls your designated website URLs or parses uploaded files (like PDFs, TXT, CSV, or DOCX). It processes the text, converts it into secure vector embeddings, and stores them to feed context directly into your AI assistant for accurate responses.

Through the dashboard, you can configure systemic prompt instructions, set custom starter suggestions, change name/agent titles, edit avatar icons, and control constraints (like limiting responses to the uploaded data only).

Yes, Askora is fully multilingual. It can read websites and documents in over 80 languages and automatically respond to users in the same language they query in.

You can upload PDFs, Word documents (DOCX), Excel/CSV spreadsheets, text files, and custom FAQ sheets. Askora parses text and formatting to index the knowledge base cleanly.

You can configure automatic crawl schedules (daily, weekly, or monthly) or manually trigger a re-crawl from the dashboard at any time to keep your chatbot's knowledge fresh as your content changes.

Our dashboard features a 'Chat Logs' module where you can review all user conversations. If the bot missed an answer, you can directly add custom Q&As to refine its performance instantly.