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Knowledge Base & RAG Engine

Ground your LLM bots with your organization's website, uploaded documents, and custom FAQs using hybrid vector retrieval and Retrieval-Augmented Generation.

What RAG Does

Grounded AI Intelligence via RAG

Instead of relying only on general model knowledge, Omnisetu retrieves live, relevant context from your knowledge sources to ground every response.

The Omnisetu RAG Pipeline Flow

Step 01

Connect Data Sources

Connect your website, PDFs, DOCX, spreadsheets, and custom FAQs.

Step 02

Automatic Indexing

Content is parsed, cleaned, and indexed for fast, accurate search.

Step 03

Customer Query

Customer asks a question via WhatsApp, Web Chat, or Social DMs.

Step 04

Smart Context Search

System retrieves exact matching policy or document sections instantly.

Step 05

Grounded AI Answer

AI generates an accurate response backed 100% by your business data.

Knowledge Sources

Multi-Source Knowledge Ingestion

Connect all your business information channels into a single, unified searchable vector index.

Website Crawler Controls

Configurable depth & automatic noise stripping

Crawling Depth Levels

Depth 1https://company.com/pricing

Crawl a single targeted URL

Depth 2Includes /pricing, /features, /about

Crawl target URL + immediate internal links

Depth 3+Full documentation & blog directory

Deep crawl across entire site domain

CSS Selectors ExcludedNoise Stripping
header
footer
nav
.privacy-policy
.cookie-banner
#sidebar-ad

Excludes navigation bars, footers, and privacy policies from entering the vector database.

Scheduled Automatic Re-indexing

Keep your AI synced with website updates automatically.

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Frequently Asked Questions

RAG is a technique that combines a vector search database with LLMs. Before generating a response, the system retrieves relevant business context (from your website, PDFs, CSVs, or FAQs) and passes that exact knowledge into the LLM prompt, ensuring responses are accurate and grounded in your company's data.
Omnisetu's website crawler supports configurable crawling depth (Depth 1, 2, or 3+) and allows you to set CSS selector exclusions (e.g., `header`, `footer`, `nav`, `.privacy-policy`) to strip out navigation menus and footers before vectorization.
Omnisetu uses a 500-token chunk size with a 50-token overlap. The 50-token overlap ensures that important context spanning chunk boundaries is not lost during retrieval.
Omnisetu applies a strict similarity threshold of 0.75. Chunks with a cosine similarity score below 0.75 are discarded, preventing weak or irrelevant context from polluting the LLM prompt.
Yes. Tables inside PDFs, CSVs, and XLSX spreadsheets are automatically parsed and converted into structured Markdown tables before vectorization, allowing structured financial or pricing data to be queried accurately.

Explore related topics

Conversational AI Platform

Learn how NLP-driven chatbots understand customer intent across channels.

AI Bots & Chatbots

Deploy RAG-grounded AI chatbots across WhatsApp, web chat, and social channels.

AI Studio — Visual Workflow Builder

Design visual conversation flows that trigger live knowledge base searches.

Function Tools

Combine RAG knowledge retrieval with real-time API function calling.

Code Tools

Execute custom JavaScript calculations on data retrieved from your knowledge base.

Omnichannel Inbox

Manage customer interactions grounded by your central RAG knowledge engine.

Your competitors are already automating. Are you?

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