Everything Enterprises Need to Know

Enterprise FAQ on RAG-as-a-Service covering deployment, security, accuracy, integrations, and benefits for AI-powered document intelligence.

FAQs

What is RAG-as-a-Service?

RAG-as-a-Service is a managed enterprise platform that connects your internal documents, knowledge sources, and databases to AI models — without needing to build your own vector database, chunking pipeline, or retrieval engine.

Upload documents → system parses, chunks, indexes, embeds → AI delivers precise answers from your verified data only.

Result: Faster AI deployment, lower engineering cost, full data privacy.

Why does my enterprise need RAG-as-a-Service?

Because generic AI models guess answers and hallucinate. RAG-as-a-Service ensures:

Zero hallucinations
Answers only from your documents
Compliance with internal policies
Faster decision-making
Lower engineering cost

Enterprises adopt RAG to protect brand, accuracy, and data privacy.

What is the difference between RAG and RAG-as-a-Service?

RAG = a technique (retrieval + generation). RAG-as-a-Service = the entire ready-to-use infrastructure:

Parsing
Chunking
Indexing
Embedding
Hybrid search
Vector DB
Access control
API integration
Compliance logging

You get the entire stack fully managed, not just the concept.

Is ChatGPT a RAG system?

No. ChatGPT is an LLM. It does not retrieve your private documents unless you manually build retrieval pipelines.

RAG-as-a-Service adds your own verified data into the AI’s reasoning.

What is the difference between RAG and an LLM?

LLM = predicts languageRAG = retrieves facts from your documents before generating answers

RAG + LLM = Enterprise-grade accuracy No hallucinations. No risks.

What are the benefits of using Cenango’s RAG-as-a-Service?

Faster go-to-market
Lower engineering cost
Reduced compliance risk
Improves productivity across departments

For Technical Teams

Fully automated parsing, chunking, indexing
REST APIs + SDKs
Custom model compatibility
Managed vector DB & hybrid search
Sub-100ms retrieval latency

For Security Teams

Zero retention
Private deployment
RBAC/SSO
SOC2, GDPR, HIPAA readiness
Full audit logs

How long does deployment take?

Most teams go live in minutes. Enterprise integrations (SSO, API connections, vector DB, compliance routing) take a few days, not months.

Is Cenango’s RAG-as-a-Service secure for enterprise data?

Yes — designed for regulated industries. Security includes:

Private VPC
Zero-retention by default
Customer-managed encryption keys
RBAC, SSO, MFA, SCIM
SOC2, GDPR, HIPAA alignment
Full audit logging
No data used to train public models

Your data. Your control. Always.

Which LLMs are compatible with RAG-as-a-Service?

Any model, including:

OpenAI GPT-4/GPT-4o
Anthropic Claude
Llama 3
Mistral
Cohere
Local models (e.g., private GPT, custom fine-tunes)

Bring your own model (BYOM) is fully supported.

What types of documents does the system support?

Everything:

PDF
Word
PowerPoint
Excel
Web pages
HTML
Scanned documents (OCR enabled)
Unstructured text
Knowledge base exports

Database dumps

How does the system ensure accuracy?

Through advanced retrieval pipelines:

Semantic chunking
Entity extraction
Keyword + vector hybrid search
Reranking using LLMs
Context de-duplication
Confidence scoring

This ensures pinpoint accuracy and no hallucinations.

Can I deploy this on-prem or in a private cloud?

Yes. Deployment options:

Private AWS/GCP/Azure
On-prem (air-gapped supported)
Hybrid
Customer VPC
Multi-region setups

What internal systems can RAG-as-a-Service integrate with?

Anything, including:

SharePoint
OneDrive
S3
GDrive
Atlassian
CMS platforms
CRMs
ERPs
Internal APIs

Custom LLMs

How do I control who can access which documents?

Using RBAC, SSO, and document-level permissions. Sync with Azure AD, Okta, Google Workspace, or any enterprise IAM.

What happens if the model cannot find the right context?

It triggers a multi-step fallback:

Expanded search
Reranking
Semantic lookup
Ask clarifying question
Escalate to human

Log gap for retraining

Is RAG-as-a-Service worth it for enterprises?

Yes. It reduces hallucinations, improves accuracy, and eliminates months of engineering cost.

What problems does RAG solve?

RAG solves hallucinations, incorrect answers, document search inefficiency, and compliance risks.

How does RAG improve accuracy?

RAG retrieves context directly from verified documents before generation.

Is RAG better than fine-tuning?

For most use cases, yes. RAG is cheaper, quicker, and more controllable.

Can RAG work with private company data?

Yes — that’s the purpose. Data is kept private, isolated, and never used for public model training.

Does RAG replace the need for knowledge bases?

No — it enhances them by making them instantly searchable via AI.

Do I need vector databases for RAG?

Not if using RAG-as-a-Service — the platform handles it automatically.

Is RAG the future of enterprise AI?

Yes. RAG enables accurate, compliant, and explainable AI.

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.