RAG Development
RAG Development Services for AI That Answers From Your Own Data
Xcrino Business Solutions is an AI-first software development company in New Delhi, India. We build retrieval-augmented generation (RAG) systems: AI assistants that find the right passages in your documents and databases and answer from them, showing where each answer came from.
Since 2016 we have served 560 clients in 26 countries, and 94% of our projects have been delivered on time and on budget. RAG projects start with a fixed-price proof of concept from $3,000 (about ₹2.7 lakh).
- RAG
- LLMs
- Vector search
- Citations
- 560
- clients served
- 26
- countries
- 69+
- developers
- 94%
- projects on time and on budget
At a glance
Key takeaways
- RAG assistants answer from your own documents and data, and show the source of each answer.
- RAG is usually faster and cheaper than fine-tuning, and easier to keep up to date.
- We test accuracy against real questions before launch, and the assistant says when it does not know.
- A fixed-price proof of concept starts from $3,000 (about ₹2.7 lakh) and takes 2–4 weeks.
- A first production release typically takes 1–2 months and starts from $5,000 (about ₹4.5 lakh).
Quick answer
What is RAG (retrieval-augmented generation)?
RAG is a way of building AI assistants that first look up relevant information in your own sources, such as documents, policies, product data or support tickets, and then generate an answer from what they found, usually with citations.
Because answers come from your current content rather than the model's memory, they stay accurate and up to date, and your team can check the source. RAG is usually faster and cheaper to build than fine-tuning a model, and many solutions combine the two.
What we build
What RAG solutions does Xcrino build?
- Internal
Internal Knowledge Assistants
Assistants that answer staff questions from policies, manuals, wikis and past projects, with links to the source.
- Customers
Customer Support Assistants
Assistants that answer customers from your help centre and product data, and hand over to a person when needed.
- Documents
Document Search and Q&A
Ask questions across contracts, reports and technical documents instead of searching them one by one.
- Languages
Multilingual RAG
Assistants for English, French, Japanese or Arabic users, tested on real content from each language and market.
- Agents
RAG Inside AI Agents
Retrieval that gives AI agents accurate company knowledge before they act.
AI agents - Quality
Evaluation and Monitoring
Test sets built from real questions, accuracy and citation checks before launch, and quality monitoring afterwards.
Fit
When is RAG the right choice?
RAG fits when people keep asking questions whose answers already exist somewhere in your documents: policies, manuals, contracts, product sheets or past tickets. If staff spend time searching shared drives, or support repeats the same answers, a RAG assistant can answer in seconds and link to the source.
It is less useful when the answer depends on calculations across live data, such as "what were sales last quarter". That needs a reporting tool or an agent that queries your database. It also cannot fix content that is missing or out of date, so we check your sources first.
Architecture
What goes into a production RAG system?
The quality of a RAG assistant depends far more on the retrieval pipeline than on the model. These are the parts we build and test.
- Ingestion: Loading PDFs, Word files, web pages, wikis, tickets and database records, with OCR for scans.
- Chunking and indexing: Splitting content into sections that keep their meaning, stored in a vector database.
- Hybrid search and re-ranking: Combining keyword and semantic search, then re-ranking results so the best passages come first.
- Access control: Users only get answers from documents they are allowed to see.
- Answer generation: Answers written from the retrieved passages, with citations and an honest "not found".
- Evaluation: Test sets from real questions to score retrieval and answers before and after each change.
Compare
RAG, fine-tuning or a plain chatbot: what is the difference?
| Approach | How it works | Best for |
|---|---|---|
| Plain chatbot | How it works: Answers from the model's general training | Best for: General questions; not your company facts |
| RAG | How it works: Looks up your content first, then answers with sources | Best for: Accurate, current answers from your documents |
| Fine-tuning | How it works: Trains a model further on examples | Best for: Changing tone, format or a narrow skill |
| RAG plus fine-tuning | How it works: Both together | Best for: High-volume assistants that need a set style and current facts |
Pricing
How much does a RAG system cost, and how long does it take?
Every project starts small and fixed-price, so you see results before you commit to a larger budget. INR figures are approximate, at about ₹90 to the US dollar.
| Stage | What you get | Typical length | Starting price |
|---|---|---|---|
| Proof of concept | What you get: A working prototype on your real data, measured results and a go or no-go recommendation | Typical length: 2–4 weeks | Starting price: From $3,000 (about ₹2.7 lakh), fixed price |
| First production release | What you get: Security, integrations, monitoring and a release your team can use every day | Typical length: 1–2 months | Starting price: From $5,000 (about ₹4.5 lakh) |
| Ongoing improvement | What you get: A dedicated AI/ML developer to improve, monitor and extend the system | Typical length: Monthly | Starting price: From $1,250 (about ₹1.13 lakh) a month |
| Running costs | What you get: Model usage billed by the AI provider, plus hosting | Typical length: Monthly | Starting price: Estimated during the proof of concept |
Process
How does Xcrino build a RAG system?
- 01
Choose sources and questions
Agree which documents and data the assistant may use, and collect real questions it must answer well.
- 02
Check and prepare the data
Our AI lead reviews whether the content can support good answers. We then clean, split and index it, with access rules.
- 03
Proof of concept
A working assistant on your real content in 2–4 weeks, from $3,000 (about ₹2.7 lakh).
- 04
Evaluate
Test accuracy and citations against the real questions, and fix gaps before anything goes live. The AI lead signs off before launch.
- 05
Launch and monitor
A first production release, usually in 1–2 months from $5,000 (about ₹4.5 lakh), then ongoing monitoring and content updates.
Accuracy
How do we keep RAG answers accurate?
- Every answer shows the passages it came from
- The assistant says it does not know when the answer is not in your sources
- Retrieval respects user permissions
- A test set built from real user questions
- Feedback buttons so users can flag wrong answers
- A schedule for re-indexing new and changed content
Related
Related services and reading
RAG often powers AI agents, and most projects start with a small test. These pages explain both.
Read: RAG vs fine-tuningAI agent developmentAI proof of conceptAI development services
Get a quote
Tell us what you want to build
Share the problem, the systems involved and your timeline. We reply with next steps and, where it fits, a fixed-price first phase.
Reach us
New Delhi, India
FAQ
RAG development: questions answered
What is RAG, and when should I use it?
RAG (retrieval-augmented generation) lets an AI model answer using your own documents and data, usually with citations. It suits knowledge assistants, support, and policy or product Q&A where answers must reflect current, company-specific information.
Should I use RAG or fine-tuning?
Use RAG when answers must come from documents that change. Use fine-tuning when you need consistent style, format or domain behaviour. Many solutions combine them, and we recommend an approach after discovery and testing.
How much does RAG development cost?
At Xcrino, a RAG proof of concept starts from $3,000 (about ₹2.7 lakh) and a first production release from $5,000 (about ₹4.5 lakh). The final price depends on how much content there is, how clean it is, and the systems the assistant must connect to.
Can a RAG assistant show where its answers come from?
Yes. We design RAG assistants to cite the documents or records each answer is based on, so your team and customers can check it.
Is my data used to train AI models?
Before any build, we agree in writing which providers process your data, where it is stored, and whether it may be used for training. If your rules require it, we scope a private or dedicated deployment.
Who owns the RAG system Xcrino builds?
You do. All source code, prompts and other assets transfer to you once the project is paid in full. Xcrino signs an NDA whenever you ask for one.
Which documents can a RAG assistant use?
PDFs, Word files, web pages, wikis, help-centre articles, tickets and database records. Scanned documents need text recognition (OCR) first, which we include where needed.
How do you measure RAG accuracy?
We build a test set of real questions with expected answers and sources, then score both retrieval and answers before launch and after every change.
Turn Your Documents Into Answers
Tell us which content your team or customers search most, and the questions they ask. We will show you what a RAG assistant could answer and what a proof of concept would cost.
Company details
Xcrino Business Solutions
RAG Development • AI Agent Development • AI Proof of Concept • Machine Learning • Custom Software • ERP
New Delhi, India | Serving clients in 26 countries
E-523, Top Floor, Sector 7, Dwarka, New Delhi 110075 • +91 99539 20493 • info@xcrino.com