Knowledge Teams
Turn policy, procedure, product, and service content into answerable knowledge.
Retrieval-Augmented Generation (RAG) 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.
RAG as a Service AEO guide
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.
IT and AI Teams
Connect LLM applications to governed retrieval, indexes, and content services.
Compliance Teams
Control sources, permissions, citations, and audit trails for AI usage.
Service Teams
Answer customer, employee, and operational questions with trusted supporting context.
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 resolution02Employee
Employee Knowledge Assistant
Answer HR, IT, finance, and operations questions from internal documentation.
Reduce help desk volume03Compliance
Policy and Regulation Q&A
Retrieve relevant policies, controls, evidence, and regulatory guidance for users.
Improve answer consistency04Research
Document Research Copilot
Summarize files, compare evidence, and cite the documents used in the answer.
Speed up analysis05Sales
Proposal Knowledge Retrieval
Find approved responses, case studies, product details, and contract language.
Accelerate proposals06API
RAG API for Applications
Embed retrieval and grounded generation into portals, apps, and automated workflows.
Scale AI safelyHow RAG as a Service works
Contellect One combines content governance, retrieval quality, AI prompts, and source citations in a production-ready pattern.
Connect Sources
Select approved documents, knowledge bases, archives, and business records.
Prepare Content
Chunk, classify, tag, index, and secure content for accurate retrieval.
Retrieve Context
Find relevant passages based on the user question, metadata, and permissions.
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.
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.
See Contellect One in action
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