Get Started

Private knowledge-grounded AI — without sending your data to public LLMs.

RAG Builder

Builds and operates production RAG pipelines over enterprise knowledge bases (documents, databases, APIs) with chunking strategies, embedding management, vector store indexing, and retrieval quality evaluation. Enables banking, telecom, and industrial enterprises to deploy private knowledge-grounded AI assistants without exposing sensitive data to public LLM providers.

Why It Matters

RAGBuilder gives enterprises the AI assistant they actually want — one that knows their documents, policies, and data — without shipping sensitive knowledge to public LLM providers.
Production-grade RAG pipelines with measurable retrieval quality turn internal knowledge bases into private, grounded AI that answers from the company’s own truth.
1.png

Grounded answers, not hallucinations

Retrieval over enterprise knowledge means the assistant cites the actual policy, contract, or manual — the difference between a usable internal tool and a liability generator.

2.png

Sensitive data stays inside

Banking, telecom, and industrial knowledge bases never leave the enterprise perimeter — private RAG delivers GenAI value where public-LLM tools are prohibited outright.

3.png

Measured quality, not vibes

Retrieval quality metrics and A/B testing — absent from no-code RAG builders — mean chunking and embedding choices are engineering decisions with evidence, not guesswork that quietly degrades answers.

The Cloudly Advantage

RAGBuilder is the GenAI product clients can see and touch — the internal AI assistant every executive has already imagined — making it our most demand-driven GenAI offering.
1.png

The GenAI use case clients ask for by name

"Chat with our documents" is the most requested enterprise AI project — BD fields inbound demand rather than creating it.

2.png

Completes the LLM triad

Fine-tuning, gateway governance, and knowledge grounding cover every enterprise GenAI architecture — a full-stack story regional competitors can't assemble.

3.png

Data-sovereignty differentiator

Private RAG inside the client perimeter wins in regulated verticals where public-LLM tools fail security review — our core BFSI and telecom advantage applied to GenAI.

4.png

Vector infrastructure services

OpenSearch, pgvector, and Pinecone/Weaviate deployment and tuning are new-generation Cluster Services engagements with ongoing operations revenue.

5.png

Knowledge-base projects expand naturally

RAG over one department's documents proves value fast — then every other department wants theirs indexed, scaling the contract with each knowledge base added.

The Final Takeaway

Pulls the whole GenAI stack — Production RAG needs LLMGateway routing, embedding pipelines through PipelineForge, and quality monitoring via DriftShield — each deployment consumes the broader platform.

Powered By

LangChain

Core to the RAGBuilder technology stack.

LlamaIndex

Core to the RAGBuilder technology stack.

pgvector

Core to the RAGBuilder technology stack.

AWS OpenSearch

Core to the RAGBuilder technology stack.

Let's start a quick, free consultation

Get Started