RAG Development Company in India: Cost and Method

Description

Service RAG development: assistants grounded in your own documents

A RAG assistant looks up the relevant passages in your documents first, then asks a language model to answer from those passages and show where the answer came from. WavX builds the ingestion, search, answer and evaluation parts as one system, and says so when a ready-made tool or a plain search box would do the job for less.

Talk to a technology expert Last updated 2 October 2026

What RAG is, and what it is for

Retrieval-augmented generation ( RAG ) is a way of making a language model answer from your documents instead of from its general training. The system searches your content for the passages that bear on the question, hands those passages to the model with the question, and instructs it to answer only from them and to say where each part came from.

WavX builds RAG assistants for four common jobs:

Customer support. Answers from your help centre, policies and product information, on the website, in the app or on WhatsApp.

Internal knowledge. Staff ask about SOPs, HR policy, contracts or technical manuals held in Google Drive or SharePoint.

Product features. "Ask this document" or "search the catalogue by description" inside software you already sell.

Sales and service desks. Agents get a drafted answer with the source beside it, and decide what to send.

RAG is one of three ways to shape what a model says. The comparison of RAG, fine-tuning and prompt engineering covers the choice. In short:

Approach

What it changes

Use it when

Prompt instructions

How the model is told to behave

The model already knows enough and only needs direction

RAG

What the model can read at the moment of the question

Answers must come from your documents and stay current

Fine-tuning

The model's own habits, through training examples

You need a consistent style or format that instructions cannot hold

How it works

A RAG system is two flows. The first runs whenever documents change. The second runs on every question.

Indexing

Collect. Connectors pull documents from where they live: Google Drive, SharePoint, a help centre, a database, a folder of PDFs.

Clean and split. Text is extracted and cut into passages along headings and paragraphs. Each passage is stored with its source, section, date and the roles allowed to read it.

Embed and index. Each passage is turned into an embedding, a list of numbers that represents its meaning, and stored in a vector index such as pgvector in PostgreSQL or Pinecone. A keyword index is kept alongside it.

Answering

Search. The question is run against both indexes, filtered by what the user is allowed to see.

Rerank. A second pass orders the candidates so the most relevant passages come first.

Answer. The model receives the question, the top passages and its instructions: answer only from these, cite them, and say so if they do not contain the answer. WavX builds this step on the OpenAI API or the Anthropic Claude API.

Show sources and log. The reply links to the documents used. The question, the passages retrieved and the answer are logged so failures can be traced.

Most of the accuracy is decided in steps 2, 4 and 5, before the model sees anything. A model given the wrong passage will write a fluent answer to the wrong question.

Why not paste everything into the prompt?

Sometimes you should. As of October 2026 Anthropic's pricing page says Claude 4.6 and later models include a 1 million token context window at standard prices. If your material is a few dozen pages, public, and rarely changes, sending all of it with each question is the simplest design, and WavX will recommend it.

It stops working as the material grows, for three reasons. You pay for every input token on every question, though prompt caching reduces this: on most Claude models a cache read is charged at 10% of the normal input price. Long prompts are slower. And you cannot show different documents to different users if everyone's question carries everything.

How accuracy is checked

WavX agrees a test set with you before the build: real questions, each tagged with the document that holds the answer and the answer your expert would accept. Search and answer are scored separately, because they fail for different reasons.

Check

Question it answers

How it is measured

Retrieval

Did the passage with the answer come back near the top?

Compared against the document tagged for each test question

Groundedness

Is every statement in the answer supported by a retrieved passage?

Read by a person, with model-graded scoring for volume

Correctness

Does the answer match what your expert would say?

Compared against the accepted answer

Refusal

When the documents do not hold the answer, does it say so?

Questions deliberately included that have no answer in the set

Permissions

Can a user ever be shown a passage they should not see?

One test account per role

This follows the evaluation guidance Anthropic publishes: make the tests specific to the task, automate grading where possible, and prefer many automatically graded questions to a few hand-graded ones. A pass mark is agreed in advance and the results are reported against it. The set is run again whenever documents, instructions or the model change.

What it costs to build

These are planning ranges from the cost model behind the AI cost calculator , not quotes. The model adds the base to the selected features, multiplies by 1.25 when the system works on documents, and gives a range from 15% below the subtotal to 25% above.

Scope

How the subtotal is reached

Planning range

Timeline band

RAG assistant over one document set

₹3,50,000 × 1.25 = ₹4,37,500

₹3,71,875 to ₹5,46,875

6–10 weeks

Plus human handoff with CRM, and an admin and analytics dashboard

(₹3,50,000 + ₹70,000 + ₹80,000) × 1.25 = ₹6,25,000

₹5,31,250 to ₹7,81,250

10–16 weeks

Assumptions: documents that contain real text, one language, one permission scheme. Scanned PDFs, large tables, several source systems and per-user permissions add work, and that work is quoted after scoping. The RAG chatbot development cost guide prints its own tiers and indicative monthly running figures.

What it costs to run

Cost

When it is charged

What drives it

Embedding

Once per passage when a document is added or changed, and once per question

Size of the document set and how often it changes

Vector index

Monthly

Number of passages; a hosted service, or pgvector inside a database you already pay for

Model usage

Per question

Passages sent with each question, multiplied by questions. The largest line at volume

Reranking, if used

Number of candidates rescored

Hosting, logs, monitoring

Traffic and how long logs are kept

Re-testing and maintenance

When documents, instructions or model versions change

How often they change

Model prices change often, so this page prints none in rupees. Use the calculator for the build and read the provider's pricing page for usage.

What goes wrong

The wrong passage is retrieved. A table was split from its heading, or the customer's words differ from the document's.

Old and new versions are both indexed. The assistant quotes last year's price list.

Scans and tables lose their structure. A scanned PDF has no text to search until it is put through text recognition, and columns can come out scrambled.

Two sources disagree and the answer blends them.

Nothing relevant is found and the model answers anyway, from general knowledge.

A restricted passage reaches the wrong user, because permissions were added after the index was built.

A document carries instructions. OWASP describes indirect prompt injection as content from an external source, such as a file, that alters the model's behaviour when it is read.

What a RAG assistant should not be trusted with

Anthropic's guidance on reducing hallucinations says its techniques reduce wrong answers and do not eliminate them. So:

It is not the final word on legal, medical, financial, safety or compliance questions. It finds and summarises. A qualified person decides.

Exact figures need the source beside them. Prices, dosages and contract amounts should be shown with the passage they came from, and checked by the reader.

It does not count. "How many contracts expire in March?" is a database query. Retrieval returns a handful of passages, not every record.

It knows only what was indexed, as of the last refresh.

When you do not need a RAG build

A small team with a few documents. As of October 2026 Anthropic's help centre says projects in Claude let you upload documents to a project knowledge base on every plan, including the free one, with a retrieval mode on paid plans for larger sets. That may be all you need.

Customer FAQs on a helpdesk you already use. Zoho SalesIQ's Answer Bot answers common questions from your knowledge base, and Intercom sells its Fin AI Agent priced per resolved outcome. Either is quicker to switch on than a build.

People only need to find the document. A search box is cheaper and never invents anything.

The knowledge is not written down, or the documents contradict each other. Fix the documents first. RAG repeats what it is given.

A custom build is worth it when permissions matter, when sources sit in several systems, when the assistant is part of your product, or when you need to measure and control accuracy yourself. If the assistant also has to hold a conversation, take actions and hand over to staff, see AI chatbot development .

RAG development is one part of AI solutions and automation at WavX.

Frequently asked questions

What is the difference between RAG and fine-tuning?

RAG gives the model your documents at the moment a question is asked, so the answer can change the day a document changes and can cite its source. Fine-tuning trains the model on examples to change how it behaves or writes. It does not keep facts current. For answering from company documents, RAG is the usual starting point.

How accurate is a RAG assistant?

It depends on your documents and questions, so we do not quote a figure in advance. Accuracy is measured on a test set of your real questions before launch, with search quality and answer quality scored separately, and reported against a pass mark agreed with you.

How much does RAG development cost?

Our cost model gives a planning range of ₹3,71,875 to ₹5,46,875 for a RAG assistant over one document set, with a 6 to 10 week timeline band. Adding human handoff and an admin dashboard moves it to ₹5,31,250 to ₹7,81,250. These are planning ranges from the AI cost calculator. A quote follows scoping.

Which vector database do you use?

We work with pgvector inside PostgreSQL and with Pinecone. If you already run PostgreSQL and the document set is moderate, pgvector avoids adding a separate service. A hosted vector database suits larger sets or teams that do not want to operate one. The choice is made on size, budget and who will maintain it.

Can it respect who is allowed to see which document?

Yes, if it is designed in from the start. Each passage is stored with the roles allowed to read it, and the search filters on the signed-in user's role before anything reaches the model. This is tested with one account per role.

Is our data used to train the AI model?

As of October 2026, OpenAI and Anthropic both state that data sent through their APIs is not used to train their models by default. Your documents are indexed in a database in your own cloud account. Only the passages needed to answer a question are sent to the model provider with that question.

Sources

Anthropic: Claude API pricing (context window, prompt caching, batch discount) · read 2 October 2026

Anthropic: Define success criteria and build evaluations · read 2 October 2026

Anthropic: Reduce hallucinations · read 2 October 2026

OWASP Top 10 for LLM Applications 2025: LLM01 Prompt Injection · read 2 October 2026

Claude Help Center: What are projects? · read 2 October 2026

Zoho SalesIQ: Zobot chatbot builder and Answer Bot · read 2 October 2026

Fin AI Agent pricing (Intercom) · read 2 October 2026

OpenAI: Data controls in the OpenAI platform · read 2 October 2026

Anthropic Privacy Center: Is my data used for model training? · read 2 October 2026

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