RAG Systems
Answers grounded in your own documents, not a model's guesswork.
Retrieval-augmented generation lets your team or customers query your actual data — policies, catalogs, tickets, contracts — and get accurate, sourced answers.
The problem
A model that doesn't know your business will guess.
General-purpose AI models are trained on public data — they don't know your internal policies, your product catalog, or your customer history, and they'll produce a confident-sounding wrong answer rather than admit it. For anything customer-facing or compliance-sensitive, that's a real risk, not a minor quirk.
Our approach
Retrieval built on your own data, with sources.
We build retrieval-augmented generation systems that search your actual documents and data — not the open internet — and ground every answer in what was retrieved, with sources attached. The result answers questions accurately because it's reading your real material, not recalling a training set.
What's included
What you get with this engagement.
Approach
How we deliver it.
- 01
Discover
We assess your documents and data — format, volume, sensitivity — and the questions people actually need answered.
- 02
Design
We design the retrieval pipeline — chunking strategy, embeddings, vector store — around your specific data.
- 03
Build
We build and evaluate the system against real queries, tuning for accuracy and relevant retrieval, not just fluent output.
- 04
Scale
We monitor answer quality and retrieval performance in production, and refine as your document set grows.
FAQ
Questions about rag systems.
RAG is an approach where an AI model retrieves relevant passages from your own documents or data before generating an answer, then bases its response on that retrieved material — instead of relying only on what it learned during training. This makes answers more accurate and traceable to a source.
It depends on the volume and complexity of your documents, and how the system needs to be accessed (internal tool vs. customer-facing). Cocoontrix RAG projects typically start from ₹25,000. Book a call and we'll scope your specific document set and use case.
For most business use cases, yes — RAG lets you update the underlying data without retraining a model, and grounds answers in real, current documents. Fine-tuning makes more sense for teaching a model a style or narrow skill, not for keeping it current on your business facts.
Yes — RAG is well suited to exactly this kind of structured internal knowledge. We handle document sensitivity and access control as part of the system design, so retrieval respects who should see what.
Ready to talk about rag systems?
Book a call or send a quick enquiry about this service — we reply within a business day.