Enterprise-Grade RAG Infrastructure

Stop building RAG, start shipping faster with RAG-as-a-Service

Your engineering team spent 6 months on retrieval pipelines. We deliver production-ready RAG-as-a-Service in days, any LLM, any data source, zero hallucinations.

Trusted by engineering teams since 2002

23+

Years in Business

1,000+

Projects Delivered

350+

Enterprise Clients

What Our Customers Say

Our clients share how we’ve made a difference in their business.

We saved 3–4 months of engineering work and launched AI 10x faster thanks to Cenango’s RAG-as-a-Service.

Deploying private RAG inside our cloud was simple and fully compliant. Security approved it instantly.

Our LLM costs dropped by nearly 60%. Accuracy improved and hallucinations fell dramatically.

Customer support is now 35% faster with RAG-powered knowledge search.

We went live in under 10 days without hiring extra ML engineers—huge win for our roadmap.

A HIPAA-compliant RAG solution that keeps all data fully isolated. Exactly what we needed.

The Problem

Building RAG yourself becomes a black hole for engineering time

6+ Months to Production

Chunking, embeddings, retrieval tuning, prompt engineering, your team spends months debugging infrastructure instead of shipping product features.

Accuracy Still Fails

Your AI sounds confident but gives wrong answers. Users lose trust, support tickets increase, and hallucinations turn into real business risks.

Costs Spiral Out of Control

Vector DB costs, ML engineers, endless maintenance as models change, what started as “we’ll build it internally” quickly becomes an expensive money pit.

The Solution

Production RAG in days, not
months

We handle the infrastructure so you can focus on what matters: your product and your customers.

Any LLM, Zero Lock-in

OpenAI, Claude, Azure, Bedrock, or self-hosted. Switch anytime without rebuilding.

Custom Integrations

Legacy systems, proprietary databases, complex workflows. We connect it all.

Enterprise Security

Data isolation, SSO, role-based access, encryption key management, zero data retention options.

Continuous Optimization

We monitor accuracy, tune retrieval, and improve performance. You ship features.

				
					// Your integration is this simple

const response = await cenango.query({
  question: "What's our refund policy?",
  sources: ["policies", "support-docs"],
  model: "claude-sonnet"
});

// Returns accurate, cited answers
// from YOUR data in milliseconds

RESULTS

What our clients achieve with RAG-as-a-Service

40%

Reduction in Hallucinations

Achieved on average within the first 30 days.

2 weeks

Average Time to Deploy

Compared to 6+ months building RAG in-house.

60%

Cost Savings

Versus maintaining and scaling internal RAG infrastructure.

Pricing

Simple, transparent pricing

Start with a pilot. Scale when you’re ready. No surprises.

$5,000 one-time

1 data source integration
Basic RAG pipeline setup
Accuracy benchmarking
Production architecture plan

$5,000 month

Multiple data sources
Up to 200K queries/month
SSO + role-based access
Ongoing optimization
Priority support

Enterprise-Grade Security & Compliance

FAQs

What is RAG-as-a-Service?

It’s a managed platform that connects your data to AI models. You don’t need to build vector databases or search systems yourself. The platform handles everything automatically, document processing, indexing, and retrieval.

Think of it as plug-and-play memory for your AI. Upload your documents and start getting accurate answers immediately.

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

RAG is the technology that combines search with AI. It’s the “what.”

Building RAG yourself means managing infrastructure, databases, and ongoing updates. That’s complex and time-consuming.

A managed service like Cenango does all that for you. Same results, zero maintenance. Most teams save 6+ months of development time.

Is ChatGPT a RAG?

No. ChatGPT is a language model.It generates responses from its training data only.

It can’t search your company documents. It doesn’t access real-time information either.

RAG changes that. It connects AI models like ChatGPT to your knowledge base. This gives you accurate, source-backed answers from your own data.

What is the difference between RAG and LLM?

An LLM generates text from patterns it learned during training. Examples include GPT-4 and Claude.

RAG adds a search step first. It finds relevant information from your documents. Then the LLM uses that information to generate responses.

This combination reduces errors significantly. Your AI can now answer questions about your proprietary data accurately.

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

Cenango specializes in retrieval technology. We continuously improve performance and accuracy. You get better capabilities without using your internal resources.

How long does it take to deploy Cenango's RAG-as-a-Service?

It depends on your needs.

Simple integrations take hours to a few days. These use standard data sources and basic workflows.

Complex implementations need 2-4 weeks. These involve custom workflows or legacy systems.

Our AI team helps establish realistic timelines upfront. We ensure smooth deployment for your specific requirements.

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

Yes. Your data stays isolated. We provide role-based access controls and SSO integration.

You can choose private deployment options.

You can also manage your own encryption keys. Zero data retention is available too.

We handle security. You focus on innovation.

Which LLMs work with Cenango's RAG-as-a-Service?

All of them. Cenango works with any AI model.

We integrate with OpenAI, Anthropic Claude, Azure OpenAI, and AWS Bedrock. Self-hosted open-source models work too.

You control which AI processes your data. Switch models anytime. No need to rebuild anything.

What’s the difference between RAG-as-a-Service and fine-tuning?

Fine-tuning teaches a model new patterns, while RAG-as-a-Service gives accurate, real-time answers from your data without retraining. Faster updates and lower maintenance.

How do you ensure data privacy and avoid hallucinations?

We use strict data isolation, encryption, and retrieval-based responses. Your data stays controlled, and answers come only from verified sources. Requirements differ by company, so contact us to learn more.

Can this run inside my VPC?

Yes, deployment options vary based on your security and infrastructure needs. We can guide you on the best setup—contact us for details.

What models do you support?

We support all major LLMs, and the best model depends on your performance, privacy, and cost goals. We’ll recommend the right setup after reviewing your requirements.

What are the starting prices?

Pricing depends on data size, integrations, security level, and deployment model.
Contact us for a tailored quote based on your use case.

Ready to ship faster?

Get a FREE architecture review. We’ll analyze your use case and show you exactly how to deploy production-ready RAG in weeks, not months.