Introducing MintVani: The first enterprise Voice AI built as One System.

Meet MintVani
Enterprise RAG
Accelerator · Enterprise RAG

Enterprise RAG & Knowledge

Grounded answers. Cited. Never invented.

True agentic retrieval over your documents, databases and knowledge bases — not naïve search. Secure, real-time pipelines return answers with citations and confidence, so every response is traceable to its source and hallucinations are engineered out.

100%
Answers cited
Real-time
Self-updating index
Any
Vector / DB source
Zero
Client data leaves your environment
Connects to Documents Vector DBs Knowledge Bases SQL / APIs
What you get

Retrieval you can audit.

Agentic RAG plans its own retrieval, ranks sources, and grounds every answer — with citations a reviewer or regulator can follow.

Agentic retrieval

The system reformulates queries, retrieves iteratively and reasons over multiple sources, not one lookup.

Citations & confidence

Every answer links to its source passages with a confidence score — full traceability by design.

Real-time freshness

Indexes update as new content arrives, so answers reflect the latest documents and data.

Permission-aware

Honours your access controls so users only ever retrieve what they're entitled to see.

Any data source

Connects to vector stores, document repositories, SQL and APIs through 150+ connectors.

Hallucination guards

If grounding is weak, the system says so or abstains — rather than inventing an answer.

How RAG answers

Question to grounded answer.

STEP 01

Understand

Interprets the question and plans what to retrieve from which sources.

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STEP 02

Retrieve

Pulls and ranks the most relevant passages across your permissioned data.

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STEP 03

Ground

Generates an answer strictly from retrieved evidence, attaching citations.

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STEP 04

Verify

Scores confidence and flags low-grounding responses for review.

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Get started

Ground answers in your own knowledge.

Point us at a document set or knowledge base and we'll return a cited, governed RAG assistant.