Enterprise RAG-as-a-Service — FAQs
RAG-as-a-Service connects your internal documents to an AI model so it answers from your verified data, not guesses. It reduces hallucination risk, supports on-prem or private cloud deployment, works with any LLM, and includes enterprise security (RBAC, SSO, SOC 2/HIPAA alignment).
FAQs
What is RAG-as-a-Service?
RAG-as-a-Service (RaaS) is a managed platform that connects your company’s internal documents and databases to an AI model, so it answers questions using your verified data instead of general internet knowledge.
You upload documents — contracts, policies, product manuals, knowledge base articles — and the system parses, chunks, indexes, and embeds them automatically. When a user asks a question, the AI retrieves the most relevant passages first, then generates an answer grounded in that retrieved content.
Example: A hospital network uploads its clinical protocols and staff handbook. A nurse asks the AI assistant, “What’s the isolation protocol for a patient with suspected MRSA?” Instead of guessing from general medical knowledge, the AI retrieves the hospital’s actual protocol document and answers from it — citing the source section.
Result: faster AI deployment, lower engineering overhead, and answers traceable back to a specific document.
Why does my enterprise need RAG-as-a-Service?
Because a general-purpose AI model has no knowledge of your internal policies, pricing, contracts, or product specifics — and will guess when asked about them.
RAG-as-a-Service addresses this by:
- Significantly reducing hallucinations by grounding responses in retrieved source documents (not eliminating them entirely — no AI system can guarantee zero errors, but grounded retrieval measurably cuts the rate of fabricated answers)
- Answering primarily from your own documents, with source attribution
- Helping enforce internal policy and compliance language consistently
- Speeding up decisions that previously required manually searching documents
- Reducing the engineering cost of building retrieval infrastructure from scratch
Example: An insurance company’s claims team uses RaaS to instantly pull the exact policy clause relevant to a claim, instead of a claims adjuster spending 20 minutes searching a shared drive.
What is the difference between RAG and RAG-as-a-Service?
RAG is a technique — retrieval plus generation.
RAG-as-a-Service is the fully managed infrastructure that implements that technique end-to-end, so your team doesn’t have to build it.
RAG-as-a-Service includes:
- Document parsing
- Chunking
- Indexing and embedding
- Hybrid (keyword + vector) search
- A managed vector database
- Access control and permissions
- API integration
- Compliance logging
Building this stack in-house typically takes an engineering team several months. RaaS gives you the same capability as a managed service.
Is ChatGPT a RAG system?
No. ChatGPT is a large language model (LLM) — it doesn’t retrieve your private documents unless you separately build a retrieval pipeline and connect it.
RAG-as-a-Service adds that retrieval layer, so the model’s answers are grounded in your verified data rather than only its general training knowledge.
What is the difference between RAG and an LLM?
An LLM generates language based on patterns learned during training. RAG retrieves relevant facts from your documents before the LLM generates a response.
Combined, RAG + LLM produces answers that are both fluent and grounded in your actual source material — which is why enterprises pair the two rather than relying on either alone.
What are the benefits of using Cenango’s RAG-as-a-Service?
- Faster time-to-market for AI-powered tools
- Lower engineering cost (no need to build retrieval infrastructure internally)
- Reduced compliance risk through document-grounded, auditable answers
- Improved productivity across departments (legal, support, operations, HR)
For technical teams:
- Fully automated parsing, chunking, and indexing
- REST APIs and SDKs
- Compatibility with custom or fine-tuned models
- Managed vector database with hybrid search
- Low-latency retrieval (typically sub-100ms for indexed queries — actual latency depends on corpus size, deployment region, and query complexity)
For security teams:
- Zero data retention by default
- Private deployment options
- Role-based access control (RBAC) and SSO
- Built for SOC 2, GDPR, and HIPAA alignment
- Full audit logging
How long does deployment take?
A basic deployment can go live in as little as a day for straightforward use cases. Enterprise-grade integrations — SSO, custom API connections, dedicated vector database provisioning, compliance routing — typically take a few days to a couple of weeks, depending on the number of systems being connected.
Is Cenango’s RAG-as-a-Service secure for enterprise data?
Yes — it’s built with regulated industries (healthcare, finance, legal) in mind. Security features include:
- Private VPC deployment
- Zero-retention by default
- Customer-managed encryption keys
- RBAC, SSO, MFA, and SCIM support
- SOC 2, GDPR, and HIPAA alignment
- Full audit logging
- Your data is never used to train public models
Which LLMs are compatible with RAG-as-a-Service?
The platform is model-agnostic. Supported models include:
- OpenAI GPT-4 / GPT-4o
- Anthropic Claude
- Meta Llama 3
- Mistral
- Cohere
- Locally hosted or fine-tuned models
Bring-your-own-model (BYOM) is fully supported, so you’re not locked into a single AI vendor.
What types of documents does the system support?
- Word (.docx)
- PowerPoint (.pptx)
- Excel (.xlsx / CSV)
- Web pages and HTML
- Scanned documents (with OCR)
- Unstructured text
- Knowledge base exports
- Database dumps
How does the system ensure accuracy?
Through a multi-step retrieval pipeline:
- Semantic chunking (splitting documents by meaning, not just length)
- Entity extraction
- Hybrid keyword + vector search
- Reranking with an LLM to surface the most relevant passages
- Context de-duplication
- Confidence scoring on retrieved results
This pipeline is designed to substantially reduce — not eliminate — the risk of incorrect or fabricated answers, by ensuring the model works from the most relevant verified passages available.
Can I deploy this on-prem or in a private cloud?
Yes. Available deployment options include:
- Private AWS, GCP, or Azure environments
- On-premises, including air-gapped environments
- Hybrid cloud
- Dedicated customer VPC
- Multi-region setups for data residency requirements
What internal systems can RAG-as-a-Service integrate with?
Common integrations include:
- SharePoint, OneDrive, Google Drive
- Amazon S3
- Atlassian (Confluence, Jira)
- CMS platforms
- CRMs and ERPs
- Internal/custom APIs
- Custom or fine-tuned LLMs
How do I control who can access which documents?
Through role-based access control (RBAC), single sign-on (SSO), and document-level permissions, synced with your existing identity provider — Azure AD, Okta, Google Workspace, or another enterprise IAM system.
Example: Your HR team’s documents remain visible only to HR-permissioned users, even though the same RAG system also serves engineering and legal.
What happens if the model cannot find the right context?
The system follows a fallback sequence rather than guessing:
- Expanded search across a broader document set
- Reranking of retrieved passages
- Semantic lookup for related concepts
- A clarifying question back to the user
- Escalation to a human agent if no confident answer is found
The gap is also logged so the retrieval system can be improved over time.
Is RAG-as-a-Service worth it for enterprises?
For most enterprises handling large volumes of internal documents, yes — it reduces hallucination risk, improves answer accuracy, and avoids months of in-house engineering work to build retrieval infrastructure from scratch.
What problems does RAG solve?
RAG addresses AI hallucinations, inaccurate answers, slow document search, and compliance risk — by grounding AI responses in your organization’s actual, verified documents.
How does RAG improve accuracy?
RAG retrieves relevant context from verified documents before the AI generates a response, so the output is anchored to real source material rather than generated purely from the model’s training data.
Is RAG better than fine-tuning?
For most enterprise use cases, yes. RAG is generally cheaper to maintain, faster to update (just add new documents — no retraining needed), and easier to audit than fine-tuning a model on proprietary data.
Fine-tuning still has a role where you need the model to adopt a very specific tone, style, or reasoning pattern — the two approaches are often combined.
Can RAG work with private company data?
Yes — that’s its primary purpose. Data stays private and isolated, and is not used to train public models.
Does RAG replace the need for knowledge bases?
No — it enhances your existing knowledge base by making it instantly searchable through natural-language questions, rather than requiring manual keyword search.
Do I need vector databases for RAG?
Not if you use RAG-as-a-Service — the platform provisions and manages the vector database for you.
Is RAG the future of enterprise AI?
RAG is currently one of the most widely adopted approaches for making enterprise AI accurate, auditable, and grounded in real company data — which is why most enterprise AI deployments today pair an LLM with some form of retrieval.
Still Have Questions?
Talk to our solutions team about your RAG-as-a-Service requirements. We’ll help you determine the best approach for your use case.