Question 1

What is a RAG pipeline and why do we need one?

Accepted Answer

RAG (Retrieval-Augmented Generation) retrieves relevant context from your documents before the LLM generates an answer. Every answer comes with citations pointing back to the source document, page, and paragraph. Without RAG, your LLM answers from memory — which means hallucination.

Question 2

How is this different from BiltIQ Manthan or BiltIQ Chat?

Accepted Answer

BiltIQ Manthan and BiltIQ Chat are ready-to-deploy products with standard configurations. This service is for organisations that need custom RAG pipelines for unusual document types, non-standard workflows, or unique compliance requirements that off-the-shelf products cannot address.

Question 3

What document formats can your RAG pipeline handle?

Accepted Answer

PDF, DOCX, XLSX, PPTX, CSV, TXT, HTML, Markdown, emails, and scanned images via OCR. We build custom parsers for domain-specific formats when needed.

Question 4

Can agents respond in Indian languages?

Accepted Answer

Yes. Our multilingual intelligence layer supports 22+ Indian languages including Hindi, Bengali, Tamil, Telugu, Marathi, Gujarati, Kannada, Malayalam, and Odia — plus 100+ global languages.

Question 5

How do you prevent hallucination?

Accepted Answer

Architecture, not prompting. RAG grounding ensures every answer is derived from retrieved document chunks. Citation linking traces each claim to its source. Confidence scoring flags low-certainty responses for human review. The LLM never answers from memory alone.

Your Documents
Become Intelligent.

Build Your Knowledge Agent

Your DocumentsBecome Intelligent.

Build Your Knowledge Agent

Your Documents
Become Intelligent.