Our Expertise

RAG Applications
(Your data into LLMs)

Q: What is RAG and why does it matter?

RAG (Retrieval-Augmented Generation) enhances large language models by retrieving relevant information from your own data sources before generating answers. This connects AI with real, contextual information so outputs are domain-specific and up-to-date.

Q: How is this different from regular generative AI?

Standard AI models rely on pre-trained data, which may be generic or outdated. RAG actively pulls relevant documents at query time and feeds them into the model, making replies grounded in your proprietary knowledge.

Q: What kinds of data can RAG work with?

RAG systems can handle PDFs, reports, wikis, ticket systems and unstructured text turning them into searchable knowledge for LLMs.

Q: Does user privacy and compliance hold up?

Yes - because RAG operates on your internal data sets, answers are drawn only from content you control, helping with compliance and auditability.

Q: Can multiple teams benefit from RAG?

Absolutely - RAG enables domain-specific copilots, such as legal query assistants, support ticket solvers, ops knowledge hubs, sales enablement bots and more.

Q: Will RAG reduce errors or ‘hallucinations’?

Yes by grounding responses in verified documents, RAG significantly lowers the chance of incorrect or fabricated answers.

We build retrieval-augmented generation systems that let AI answer questions from your own documents, not the public internet.

Get Your RAG Application

Chat with Any Document

Compliance-ready assistant

Policy-aware answers

Domain-specific copilots

Our Solutions

NVIDIA Powered voice based Agentic RAG

Agentic RAGVoice AI

An NVIDIA-powered, voice-enabled agentic RAG system that transforms documents into grounded conversational experiences, combining semantic retrieval, agentic reasoning, and natural speech output for accurate, accessible enterprise knowledge interaction.

NVIDIA AIMultimodal AI

Chat With Any Document

Conversational AIRetrieval-Augmented Generation

Chat with Anything is an AI-powered RAG platform that turns documents into interactive conversations, enabling fast, accurate insight extraction across formats with real-time chat, personalization, and transparent usage tracking.

Semantic SearchMulti-Format Documents

RAG-Powered Compliance Assistant

Compliance AIRAG Architecture

A RAG-powered compliance assistant that delivers accurate, cited answers to financial regulation queries by grounding AI responses in authoritative sources, ensuring auditability, reduced risk, and faster decision-making for enterprises.

Financial RegulationsAudit-Ready AI

RAG-Powered Enterprise Knowledge Agent

Enterprise Knowledge AgentRetrieval-Augmented Generation

A RAG-powered enterprise knowledge agent that unifies internal documents, retrieves relevant information via semantic search, and delivers grounded, real-time answers to accelerate decision-making with accuracy, security, and transparency.

Decision Support AISemantic Search

Our Highlights

Grounded answers from your documents

AI responses powered by your own PDFs, wikis, tickets and reports, not generic internet data.

Secure company-specific context

Compliant assistants that generate answers strictly based on your internal content.

Domain-specific copilots

Tailored AI helpers for legal, support, operations, sales and more.

Reduced hallucinations

RAG systems pull trusted context so responses are relevant and accurate.

Faster insights

Works across unstructured datasets for instant, query-based knowledge retrieval.

Why Choose GenAI Protos for RAG Applications (Your Data into LLMs)

We partner with you end-to-end from shaping the right use-cases to shipping secure, scalable systems that your teams can confidently adopt.

Strategic GenAI partner

Production-ready systems

We build solutions that answer from your data not the public web.

Designed for enterprise workflows from tickets to policies.

Customized copilots for your teams - legal, support, ops, sales.

Robust RAG pipelines optimized for security and relevance.

Scalable and extensible - grow with your knowledge needs.

Our Expertise

RAG Applications
(Your data into LLMs)

We build retrieval-augmented generation systems that let AI answer questions from your own documents, not the public internet.

Get Your RAG Application

Chat with Any Document

Compliance-ready assistant

Policy-aware answers

Domain-specific copilots

Our Solutions

Our Highlights

Grounded answers from your documents

AI responses powered by your own PDFs, wikis, tickets and reports, not generic internet data.

Secure company-specific context

Compliant assistants that generate answers strictly based on your internal content.

Domain-specific copilots

Tailored AI helpers for legal, support, operations, sales and more.

Reduced hallucinations

RAG systems pull trusted context so responses are relevant and accurate.

Faster insights

Works across unstructured datasets for instant, query-based knowledge retrieval.

Why Choose GenAI Protos for RAG Applications (Your Data into LLMs)

We partner with you end-to-end from shaping the right use-cases to shipping secure, scalable systems that your teams can confidently adopt.

Strategic GenAI partner

Production-ready systems

We build solutions that answer from your data not the public web.

Designed for enterprise workflows from tickets to policies.

Customized copilots for your teams - legal, support, ops, sales.

Robust RAG pipelines optimized for security and relevance.

Scalable and extensible - grow with your knowledge needs.

FAQs about the GenAI Protos services

Answering common questions about GenAI Protos to help you get started

What is RAG and why does it matter?

How is this different from regular generative AI?

What kinds of data can RAG work with?

Does user privacy and compliance hold up?

Can multiple teams benefit from RAG?

Will RAG reduce errors or ‘hallucinations’?

Get Your RAG Solution Started

Book a Free Demo

Schedule a Call

FAQs about the GenAI Protos services

Answering common questions about GenAI Protos to help you get started

What is RAG and why does it matter?

How is this different from regular generative AI?

What kinds of data can RAG work with?

Does user privacy and compliance hold up?

Can multiple teams benefit from RAG?

Will RAG reduce errors or ‘hallucinations’?

Get Your RAG Solution Started

Book a Free Demo

Schedule a Call

RAG Applications (Your data into LLMs)