Retrieval-Augmented Generation as a Service

Ground AI answers in trusted enterprise content with secure Retrieval-Augmented Generation.

Connect enterprise documents and knowledge bases to LLMs with governed RAG as a Service for accurate, cited, permission-aware AI answers.

What is Retrieval-Augmented Generation (RAG) as a Service?

Retrieval-Augmented Generation, or RAG, is an AI architecture that retrieves trusted enterprise content before generating an answer. RAG as a Service in Contellect One connects governed documents, knowledge bases, records, and metadata to large language models so answers are grounded, cited, permission-aware, and easier to trust.

Groundedanswers use approved enterprise sources instead of model memory alone
Citedresponses can link back to source documents and evidence
Secureretrieval respects roles, permissions, and content governance

Built for enterprise AI teams

RAG needs more than a chatbot. It needs trusted content pipelines, retrieval quality, access control, and clear answer governance.

01

Knowledge Teams

Turn policy, procedure, product, and service content into answerable knowledge.

02

IT and AI Teams

Connect LLM applications to governed retrieval, indexes, and content services.

03

Compliance Teams

Control sources, permissions, citations, and audit trails for AI usage.

04

Service Teams

Answer customer, employee, and operational questions with trusted supporting context.

Featured workflow

Grounded Enterprise AI Answers

Index approved content, retrieve the most relevant passages, apply permissions, and generate answers with source citations.

  • Connect documents, archives, knowledge bases, and business records.
  • Reduce hallucinations by grounding responses in trusted enterprise content.
  • Expose answers through portals, assistants, search, or workflow experiences.
OutcomeDeploy useful AI answers without losing source control, compliance, or trust.
01Support

Customer Service Assistant

Answer service questions using approved product, policy, contract, and knowledge content.

Improve first-contact resolution
02Employee

Employee Knowledge Assistant

Answer HR, IT, finance, and operations questions from internal documentation.

Reduce help desk volume
03Compliance

Policy and Regulation Q&A

Retrieve relevant policies, controls, evidence, and regulatory guidance for users.

Improve answer consistency
04Research

Document Research Copilot

Summarize files, compare evidence, and cite the documents used in the answer.

Speed up analysis
05Sales

Proposal Knowledge Retrieval

Find approved responses, case studies, product details, and contract language.

Accelerate proposals
06API

RAG API for Applications

Embed retrieval and grounded generation into portals, apps, and automated workflows.

Scale AI safely

How RAG as a Service works

Contellect One combines content governance, retrieval quality, AI prompts, and source citations in a production-ready pattern.

1

Connect Sources

Select approved documents, knowledge bases, archives, and business records.

2

Prepare Content

Chunk, classify, tag, index, and secure content for accurate retrieval.

3

Retrieve Context

Find relevant passages based on the user question, metadata, and permissions.

4

Generate Answers

Produce grounded responses with source context, citations, and monitoring.

RAG as a Service benefits

More trustworthy AI

Responses are grounded in current enterprise content and can show supporting sources.

Faster deployment

Teams avoid building every retrieval, indexing, and governance component from scratch.

Safer adoption

Permissions, auditability, and controlled source selection reduce AI governance risk.

See Contellect One in action

See how Contellect One captures, governs, searches, and automates documents in one secure workspace tailored to your business.

  • Explore your document and workflow use case
  • See AI extraction, search, and automation in action
  • Review deployment, governance, and integration options
Request a DemoExplore the Platform

RAG as a Service FAQs

What does RAG mean?

RAG stands for Retrieval-Augmented Generation. It retrieves trusted content first, then uses that content to generate a grounded AI answer.

Why is RAG better than a standalone chatbot?

A standalone chatbot may rely on general model knowledge. RAG connects answers to your current enterprise documents and can include citations.

Can RAG respect document permissions?

Yes. A governed RAG implementation should retrieve only content the user is authorized to access.

What content works best for RAG?

Policies, procedures, product guides, contracts, knowledge articles, FAQs, manuals, records, and approved documentation are strong sources.