RAG Chatbot Development Cost, Architecture & Guide (2026)

A RAG chatbot costs ₹2,00,000 to ₹18,00,000 to build in India in 2026. A single-source assistant over a few hundred documents lands at ₹2L–₹5L; a production system with permissions, multiple sources and evaluation runs ₹5L–₹12L; and a multi-tenant or regulated deployment starts around ₹12L. Running costs add ₹15,000–₹1,20,000 a month, driven almost entirely by how much text you push into each query.

Key takeaways

• Most Indian businesses land at ₹4,00,000 to ₹9,00,000 for a RAG assistant that answers reliably from their own documents.
• Retrieval quality, not model choice, decides whether it works. Roughly 60–70% of the build is data preparation, chunking and evaluation — the model is the cheap part.
• The single largest running cost is context size. Retrieving three relevant passages instead of forty pages routinely cuts token spend by half or more with no loss of answer quality.
• Permissions are the most under-scoped requirement. If different users may see different documents, that must be filtered at query time — retrofitting it later is close to a rebuild.
• Under a few hundred stable documents, a well-configured off-the-shelf tool may genuinely serve you better than a custom build. Say no to the build if that is your situation.

What RAG Actually Is, and Why It Costs What It Costs

Retrieval-Augmented Generation means the chatbot searches your documents first and answers only from what it found, instead of answering from the model's general training. That single architectural choice is what separates a system you can put in front of customers from one that invents policy confidently.

The cost follows directly from that pipeline. Every stage between "your documents" and "a grounded answer" is engineering: extracting text from whatever format it lives in, splitting it sensibly, converting it to embeddings, storing those in a searchable index, retrieving the right passages for a given question, assembling them into a prompt, and checking the answer actually came from them. The language model appears at the very end and is the least of it.

Pipeline stage	Share of build	What goes wrong when it is skipped
Document extraction	10% – 20%	Scanned PDFs and tables come out as noise
Chunking strategy	10% – 15%	Retrieves the right document, answers the wrong question
Embedding and indexing	8% – 12%	Semantically close questions miss their answers
Retrieval and re-ranking	15% – 25%	Right passage exists but never surfaces
Prompt assembly and grounding	10% – 15%	Model ignores the context and improvises
Evaluation harness	10% – 15%	No way to tell whether a change helped
Application and interface	15% – 25%	Auth, logging, admin, feedback capture

RAG Chatbot Cost Tiers in India

Tier	Cost	Timeline	Scope
Single-source assistant	₹2,00,000 – ₹5,00,000	4 – 7 weeks	One clean source, a few hundred documents, no per-user permissions
Production multi-source	₹5,00,000 – ₹12,00,000	8 – 16 weeks	Several sources, permissions, re-ranking, evaluation, monitoring
Multi-tenant or regulated	₹12,00,000 – ₹18,00,000+	4 – 8 months	Tenant isolation, audit trails, DPDP controls, human review workflow
Enterprise knowledge platform	₹18,00,000+	6 – 12 months	Organisation-wide, governed, integrated with identity and access systems

The jump from the first tier to the second is caused by two things almost nobody scopes upfront: permissions, and the evaluation harness. A demo that answers questions from a shared folder is genuinely straightforward. A system where a junior employee must not see the salary policy, and where you can prove a prompt change improved accuracy rather than assuming it, is a different piece of software.

Where the Running Cost Actually Goes

Line	Monthly cost	Scales with
Model API usage	₹6,000 – ₹90,000	Queries × context size — context is the multiplier
Embedding generation	₹500 – ₹12,000	Document churn, not query volume
Vector database hosting	₹3,000 – ₹45,000	Corpus size and required latency
Application hosting	₹3,000 – ₹30,000	Traffic
Re-indexing pipeline	₹1,000 – ₹15,000	How often your content changes
Monitoring and evaluation runs	₹2,000 – ₹20,000	How seriously you take quality

Notice that embeddings are cheap and recurring model calls are not. Teams often worry about the cost of indexing a large corpus — it is usually a few thousand rupees once. The bill that grows is the one where every question drags forty pages of context into a frontier model. That is an engineering problem with an engineering fix, and it is the highest-leverage optimisation available in the whole system.

Chunking: The Cheapest Decision With the Biggest Effect

Chunking is how your documents are split before indexing, and it decides more about answer quality than any other single choice. Split too small and a passage loses the context that made it meaningful. Split too large and retrieval returns a wall of text where only two lines were relevant, inflating both cost and confusion.

Content type	Approach that works	Common mistake
Policy documents	~300 words with 50-word overlap, split on headings	Splitting mid-clause so conditions detach from rules
Product catalogues	One structured record per product, not prose	Treating a spec table as a paragraph
FAQs and support articles	One chunk per question-answer pair	Merging twenty FAQs into one chunk
Contracts and legal	Clause-level, preserving section numbering	Losing the numbering that gives clauses meaning
Tables and spreadsheets	Row-level with column headers repeated	Flattening to text and losing which column a value belonged to
Long-form manuals	Section-level with a parent-section summary attached	Fixed-size splitting that ignores structure

The last row is the default in most quick implementations — fixed 500-token chunks with no regard for document structure — and it is the most common cause of a RAG system that "sort of works". Structure-aware chunking costs perhaps ₹40,000 to ₹1,20,000 more and is the difference between 70% and 90% accuracy on the same corpus.

Permissions: The Requirement That Doubles the Price

If every user may see every document, RAG is comparatively simple. The moment that stops being true — HR policies staff should not read, client files only their account manager should see, salary bands, unreleased pricing — the architecture changes materially.

The wrong implementation retrieves first and filters afterwards. It looks correct in testing and leaks in production, because the model has already seen the passage before your filter ran, and a determined question can surface it. The right implementation filters at query time, so restricted content is never retrieved for a user who lacks access.

Permission model	Added cost	What it requires
Everyone sees everything	₹0	Nothing — but be certain it is true
Role-based (a few groups)	₹60,000 – ₹1,80,000	Access metadata on every chunk, filtered at query
Per-document ownership	₹1,20,000 – ₹3,50,000	Per-record ACLs, kept in sync with the source system
Inherited from an existing system	₹2,00,000 – ₹6,00,000	Live sync with SharePoint/Drive/ERP permissions
Multi-tenant isolation	₹2,50,000 – ₹7,00,000	Tenant filtering at query level plus isolation testing

The synchronisation problem in row four is the one that bites. Permissions in your source system change daily; if your index only learns about that on a nightly re-run, there is a window where a revoked user still gets answers from documents they no longer have access to. Decide explicitly how fresh permissions must be, because "eventually" is a security posture whether or not anyone chose it.

Retrieval Quality: Why Good Documents Still Give Bad Answers

Basic vector search finds passages that are semantically similar to the question. That is often not the same as passages that answer it. Three techniques close most of that gap, and a quote that includes none of them is quoting a prototype.

• Hybrid search (₹40,000 – ₹1,20,000). Combines semantic similarity with keyword matching. Essential when your domain has exact terms — product codes, section numbers, drug names — that embeddings blur together.
• Re-ranking (₹50,000 – ₹1,50,000). Retrieve twenty candidates cheaply, then use a smaller model to rank which three actually answer the question. Usually the single biggest accuracy improvement available per rupee.
• Query rewriting (₹30,000 – ₹90,000). A user asks "what about returns after a month?" — the system rewrites it into a self-contained query before searching. Fixes most multi-turn failures.

Together these typically move a system from around 70% to the high 80s or low 90s on a graded test set. If a vendor's proposal jumps from "embed the documents" straight to "the chatbot answers", ask which of these three they are including.

Evaluation: How You Know It Works

A RAG system without an evaluation set cannot be improved, only fiddled with. Every prompt change becomes an opinion, and quality drifts without anyone noticing until a customer complains.

What to measure	How	Healthy range
Retrieval hit rate	Did the correct passage appear in the top results?	85% – 95%
Answer correctness	Graded against known-good answers	85% – 95%
Groundedness	Is every claim traceable to a retrieved passage?	Above 95%
Refusal accuracy	Does it decline when the answer is not in the corpus?	Above 90%
Cost per query	Tokens in and out, per question	Track it; it drifts upward

Groundedness is the metric that matters most and gets measured least. A system can be 90% "correct" while inventing the reasoning behind its answers — which is fine until someone asks where a figure came from. Building a 150–250 question evaluation set costs ₹50,000 to ₹1,50,000 and is the item most often cut from a quote to win the deal. Cutting it is how a project ends up costing more, because every subsequent change requires manual regression testing by hand, indefinitely.

What Your Documents Need to Look Like

Retrieval quality is bounded by content quality. If three PDFs state different refund windows, no model resolves that for you — it picks one and states it confidently.

Source condition	Preparation cost	Effect if ignored
Clean, structured, current	₹20,000 – ₹60,000	—
Contradictory across documents	₹60,000 – ₹2,50,000	Confident wrong answers, unpredictably
Undated, no version history	₹40,000 – ₹1,50,000	Answers from superseded policy
Scanned image PDFs	₹80,000 – ₹3,00,000	OCR noise indexed as fact
Heavy tables and spreadsheets	₹60,000 – ₹2,00,000	Numbers detached from their headers
Knowledge only in people's heads	₹80,000 – ₹3,00,000	The most-asked questions have no source at all

That last row is real in most Indian SMBs and is worth doing regardless of the chatbot — writing down what one senior person knows is valuable on its own, and it is usually the single biggest accuracy lever available.

RAG vs Fine-Tuning vs Long Context

	RAG	Fine-tuning	Long context
Upfront cost	₹2L – ₹18L	₹3L – ₹12L on top	Low build, high running
Updating content	Re-index — minutes	Retrain — repeats the cost	Immediate
Cost per query	Low — retrieves a few passages	Low	Very high — pushes everything every time
Traceable sources	Yes	No	Partially
Right for	Facts, policies, catalogues — most cases	Tone and format at volume	Small, stable corpora

Long context — simply pasting all your documents into every prompt — is genuinely the right answer when your corpus is small and rarely changes. Under roughly fifty pages, it is cheaper to build and simpler to maintain than a retrieval pipeline, and anyone selling you RAG for that is overselling. Past a few hundred pages the economics invert sharply, because you pay to reprocess the entire corpus on every single question.

Build vs Buy for RAG

	Off-the-shelf tool	Framework-assisted build	Custom pipeline
Upfront	₹0 – ₹1,00,000 setup	₹2L – ₹8L	₹5L – ₹18L
Monthly	₹5,000 – ₹2,00,000	₹15,000 – ₹60,000	₹20,000 – ₹1,20,000
Permissions	Usually basic or none	Possible	Full control
Your data used for their model	Read the terms carefully	No	No
You own	Nothing	The pipeline	Everything

Be honest about which row you are in. A stable corpus of a few hundred documents, no permission rules, and internal users only — an off-the-shelf tool will serve you well and a ₹6,00,000 build is not justified. Deep integration, per-user permissions, an accuracy bar you must be able to prove, or customer-facing use: that is where a build earns its cost, because you own the pipeline and can fix the parts that matter. WavX Solutions builds your own software in a fully custom way, with your own pricing model — including telling you plainly when the simpler option is the right one.

Cutting the Running Cost Without Losing Accuracy

A RAG system's monthly bill is almost entirely a function of how much text you send the model per question. Seven techniques, in rough order of return per rupee of engineering.

1. Retrieve fewer, better passages. Three well-ranked chunks beat fifteen loosely relevant ones on both accuracy and cost. This is what re-ranking buys you.
2. Route by difficulty. Classify the question first; send the straightforward majority to a small model and reserve the frontier model for genuinely hard ones. Commonly halves spend with no measurable quality change.
3. Cache repeated questions. In most support corpora a small number of questions account for a large share of traffic. Caching those answers is close to free.
4. Cap context length hard. A ceiling on retrieved tokens per query prevents a single pathological question from costing hundreds of rupees.
5. Summarise long chunks at index time, not query time. You pay once instead of on every retrieval.
6. Strip boilerplate before indexing. Headers, footers, disclaimers and navigation repeated across 400 documents waste retrieval slots and tokens on every query.
7. Re-index only what changed. Full re-indexing on a schedule is wasteful once the corpus is large; change detection pays for itself quickly.

Optimisation	Build cost	Typical saving
Re-ranking to fewer passages	₹50,000 – ₹1,50,000	30% – 50% of token spend
Difficulty-based model routing	₹60,000 – ₹1,80,000	40% – 60%
Answer caching	₹30,000 – ₹80,000	10% – 30%
Context caps	₹15,000 – ₹40,000	Prevents outliers, not average cost
Boilerplate stripping	₹25,000 – ₹70,000	10% – 20%

RAG Chatbot Cost by Use Case

Use case	Cost	The hard part
Customer support over help articles	₹2.5L – ₹6L	Keeping answers current as policies change
Internal policy and HR assistant	₹3L – ₹8L	Permissions — who may see what
Product and catalogue Q&A	₹3L – ₹9L	Structured data does not chunk like prose
Sales enablement over collateral	₹3L – ₹7L	Version control — old decks give old pricing
Contract and legal document search	₹6L – ₹15L	Clause-level precision; liability boundaries
Technical documentation assistant	₹4L – ₹10L	Code blocks, versions, deprecated APIs
Regulatory / compliance knowledge base	₹8L – ₹18L	Auditability and traceable citation of source

The internal-assistant row is consistently the best first project. The audience is forgiving, the content is already yours, mistakes are recoverable, and the permission model forces you to build the hard part properly on a low-stakes system rather than discovering it in front of customers.

Compliance: DPDP and RAG

RAG systems have a specific compliance shape because they copy your data into a second store — the vector index — and then send fragments of it to a third-party model API.

Obligation	What it means for a RAG build	Cost
Right to erasure	Delete must reach the source, the index, the logs and the backups	₹60,000 – ₹2,00,000
Cross-border transfer	Personal data in a retrieved passage leaves India when the prompt is sent	₹40,000 – ₹1,50,000 for redaction
Purpose limitation	Documents indexed for support cannot silently power sales	Design work
Retention limits	Conversation logs need an automated purge	₹30,000 – ₹80,000
Audit trail	Which passages produced which answer, for how long	₹50,000 – ₹1,80,000

Erasure is the genuinely hard one. A customer's personal data may sit inside a chunk that was embedded, indexed, cached and logged. Deleting the source row does not touch any of those copies. Design the deletion path at the start; retrofitting it means re-architecting how chunks trace back to source records.

Security Risks Specific to RAG

• Indirect prompt injection. If you index documents that outsiders can influence — supplier emails, uploaded files, scraped pages — an attacker can place instructions inside them. The retrieved passage then carries an instruction into your prompt. Treat retrieved content as untrusted data, never as instructions.
• Cross-tenant retrieval. In a multi-tenant product, a missing tenant filter is a data breach, not a bug. Filter at query time and write an isolation test that would fail loudly.
• Over-broad indexing. Pointing the indexer at an entire shared drive is fast and routinely pulls in salary sheets, resignation letters and legal correspondence nobody meant to expose.
• Cost-based abuse. An unauthenticated endpoint with no rate limit is an invitation to run up your model bill.
• Citation spoofing. A model can cite a source that does not support its claim. Verify groundedness programmatically rather than trusting the citation to be honest.

The first item is the one most security reviews miss entirely, because it does not look like a conventional injection. Budget ₹80,000 to ₹2,50,000 for RAG-specific security work on anything customer-facing.

Timeline and Team

Phase	Duration	Output
Corpus audit	3 – 7 days	What exists, in what state, with what permissions
Extraction and cleaning	1 – 3 weeks	Normalised text, contradictions flagged
Chunking and indexing	1 – 2 weeks	Structure-aware chunks, embeddings, index
Retrieval tuning	1 – 3 weeks	Hybrid search, re-ranking, query rewriting
Evaluation build	1 – 2 weeks	150 – 250 graded questions with known answers
Application layer	2 – 4 weeks	Interface, auth, permissions, logging, feedback
Hardening and pilot	2 – 3 weeks	Security tests, load tests, limited rollout

Ten to sixteen weeks with three people — a backend engineer, an AI engineer, and someone who owns the content. That third role is the one companies forget to staff, and it is the one that determines whether the system is accurate, because no engineer can adjudicate which of your two conflicting refund policies is current.

Questions to Ask a RAG Vendor

1. Show me your evaluation set for a comparable project, and the scores.
2. What is your chunking strategy for my content type, and why that one?
3. Do you use hybrid search and re-ranking, or plain vector search?
4. How does the system refuse when the answer is not in the corpus?
5. How do permissions work, and are they filtered at query time or after retrieval?
6. How do you measure groundedness, not just correctness?
7. What is my estimated token cost per query at my expected volume?
8. How do I re-index when documents change, and how fast does that propagate?
9. How is personal data handled before a prompt reaches a third-party API?
10. Do I own the pipeline, the prompts, the index and the evaluation set?

Question five separates a system that has been built for production from one that has been demoed. "We filter the results after retrieval" is the wrong answer, and it is the common one.

Common Mistakes

• Indexing everything. More documents is not better retrieval; it is more noise competing for the same three slots. Curate.
• Fixed-size chunking on structured content. Catalogues and tables need record-level treatment, not 500-token slices.
• No refusal behaviour. A system that always answers will always answer wrongly when it does not know.
• Skipping re-ranking to save ₹1,00,000. Usually the single largest accuracy improvement available, and it lowers token cost at the same time.
• Treating the index as build-once. Content changes; an index that does not is a system that confidently quotes last year's pricing.
• No feedback capture. A one-click "this was wrong" that feeds your evaluation set turns every user into a tester, for almost no cost.

A Realistic First-Year Budget

Item	Year 1
Corpus audit and cleaning	₹60,000 – ₹3,00,000
Build (production multi-source)	₹5,00,000 – ₹12,00,000
Evaluation set and harness	₹50,000 – ₹1,50,000
Security and compliance	₹80,000 – ₹3,00,000
Running costs (12 months)	₹1,80,000 – ₹14,40,000
Tuning and content operations	₹1,20,000 – ₹5,00,000
First-year total	₹9.9L – ₹38.9L

So What Should You Budget?

For a production RAG assistant over your own documents — several sources, working permissions, measured accuracy — plan ₹5,00,000 to ₹9,00,000 and ten to sixteen weeks, plus ₹20,000 to ₹70,000 a month to run. Below ₹2,50,000 you are buying a prototype: it will demo well and lack the retrieval tuning, permissions and evaluation that make it dependable. Above ₹12,00,000 you are buying multi-tenancy, regulatory controls or a scale of corpus that genuinely justifies it.

Three decisions determine whether that money produces something people use. Spend on retrieval, not on the model — re-ranking and structure-aware chunking are where accuracy comes from. Build the evaluation set on day one, because without it you cannot tell improvement from regression. And settle permissions before anything is indexed, because it is the one requirement that is genuinely expensive to add later.

What you should end up owning is a pipeline: your documents, your index, your prompts, your evaluation suite, on infrastructure in your name. That is what makes the second use case cost a fraction of the first — and it is why WavX Solutions builds your own software in a fully custom way, engineered around your actual content and workflow, with a pricing model that fits your business rather than one that charges you more as you use it.

More on Building RAG Systems in India

RAG chatbot development cost in 2026 typically ranges from ₹2-5 lakh for a single-source assistant to ₹10-15 lakh or more for enterprise deployments, with most business bots landing around ₹5-10 lakh. The price depends on how many data sources you connect, your accuracy needs, and the channels you support. A RAG chatbot is worth it when you need answers grounded in your own documents rather than generic replies.

WavX Solutions builds RAG chatbots on OpenAI GPT and Anthropic Claude, with LangChain orchestration and vector databases on AWS, tuned for Indian businesses. Here is the full breakdown.

What Is a RAG Chatbot?

RAG stands for Retrieval Augmented Generation. Instead of relying only on what an LLM learned during training, a RAG chatbot retrieves the most relevant pieces of your own content, your product docs, policies, PDFs or website, and passes them to the model to generate an answer grounded in that content.

The payoff is accuracy. The bot answers from your latest information, cites its sources, and hallucinates far less than a plain LLM. This makes RAG the standard architecture for support bots, internal knowledge assistants and documentation search.

Want it built your way? WavX Solutions creates your own software in a fully custom way — engineered around your exact workflow, with a pricing model that fits your business. Contact now → or email buildwith@wavxsolutions.in.

How RAG Architecture Works

A RAG pipeline has a few core stages:

1. Ingestion: your documents are cleaned, split into chunks and converted into embeddings (numeric representations).
2. Storage: those embeddings are stored in a vector database like Pinecone, Qdrant, Weaviate or pgvector.
3. Retrieval: when a user asks a question, the system finds the most similar chunks.
4. Generation: the retrieved chunks plus the question go to GPT or Claude, which writes a grounded answer.
5. Guardrails: filters, citations and fallbacks keep the output safe and honest.

Getting retrieval quality right, chunk size, embedding model and reranking, is where much of the engineering value sits. Explore our AI solutions for how we tune each stage.

RAG Chatbot Development Cost Tiers

Tier	Scope	Typical INR cost	Timeline
Single-source assistant	One knowledge base (docs or website)	₹2-5 lakh	3-5 weeks
Multi-source business bot	Several sources, integrations, admin panel	₹5-10 lakh	6-10 weeks
Enterprise RAG platform	Multi-tenant, analytics, access control	₹10-15 lakh+	3-5 months

WavX Solutions builds each of these custom, with no templates, so your quote reflects your real data and integrations.

What Affects the Cost

• Number and format of sources. Clean text is cheap; scanned PDFs and images need OCR and preprocessing.
• Volume of documents. More content means larger vector indexes and higher storage.
• Accuracy and reranking. Higher precision needs rerankers and evaluation, adding effort.
• Channels. Web widget, WhatsApp, Slack or in-app each add work.
• Access control. Role-based answers and per-user data isolation raise complexity.

Running Costs of a RAG Chatbot

Cost item	Typical monthly INR
LLM tokens (GPT / Claude)	₹5,000-50,000
Vector DB + embeddings	₹3,000-25,000
Hosting and monitoring (AWS)	₹4,000-30,000

Costs scale with traffic. WavX Solutions keeps them low with caching, smaller embedding models where suitable, and routing easy queries to cheaper LLMs.

Common Use Cases

• Customer support: answer product and policy questions from your help centre 24/7.
• Internal knowledge: let staff query HR policies, SOPs and contracts instantly.
• E-commerce: product discovery and specification questions grounded in your catalogue.
• Education: course assistants that answer from your material.
• Legal and finance: document search with citations and strict guardrails.

Most of these fall in the ₹5-10 lakh business-bot band once integrations are included.

Why WavX Builds on GPT and Claude

We choose the model per use case. Anthropic Claude often shines on long-document reasoning and careful, grounded answers, while OpenAI GPT is strong on broad tasks and tooling. Building on both, with LangChain, means WavX Solutions can route each query to the best-fit model and swap providers if pricing or performance changes, protecting your investment.

As a remote-first company founded in 2022 with custom projects, WavX Solutions serves clients across India with ₹/INR pricing, GST invoicing and UPI-friendly billing. See our full custom software capability.

Getting Started

We begin with a data audit and a free scoping call to understand your sources, then propose an architecture and INR range. You get a phased plan with a pilot before full rollout. Read more about WavX or get a free quote.

How this compares to other builds WavX has costed

Build type	Typical low	Typical high	Midpoint
How to Make an Ecommerce Website in India	₹20K	₹8L	₹4.1L
AI Agent Development Cost in India (2026)	₹1.5L	₹10L	₹5.8L
RAG Chatbot Development Cost, Architecture & Guide (2026) (this guide)	₹2L	₹18L	₹10L
How to Build an AI Agent for Your Business 2026	₹5L	₹50L	₹27.5L
Fintech App Development Cost in India	₹5L	₹8L	₹6.5L

Compiled from 114 build types costed across the WavX guides. Figures are the published ranges from each linked guide, not quotes — your own number depends on scope, integrations and timeline.

Where this sits across every build WavX has costed

Benchmark	Midpoint cost
Cheapest quartile (25th percentile)	₹3.3L
Median of all 114 costed builds	₹4.8L
Most expensive quartile (75th percentile)	₹5.8L
This build	₹10L

This build is more expensive than 83% of the 114 build types costed across this site — the most expensive quartile. Derived from the published ranges in our own guides, recomputed on every rebuild.

Three-year cost of ownership

Line item	Low	High
Initial build (year 1)	₹2L	₹18L
Maintenance, per year after year 1	₹30K	₹4.5L
Total over three years	₹2.6L	₹27L

A model, not a quote. Build figures are this guide's own range; maintenance is the 15–25% of build cost per year we publish in our app maintenance cost guide, applied to years 2 and 3 (year one is covered by the build). Typical delivery for this size of build is 10–16 weeks. Your own number depends on scope — tell us what you are building and we will price it properly.

Frequently Asked Questions

How much does RAG chatbot development cost?

A RAG chatbot typically costs around ₹2-5 lakh for a single-source assistant, ₹5-10 lakh for a multi-source business bot, and ₹10-15 lakh or more for enterprise deployments. WavX Solutions quotes based on your data sources and integrations.

What is RAG in a chatbot?

RAG stands for Retrieval Augmented Generation. The chatbot first retrieves relevant chunks from your documents using a vector database, then feeds them to an LLM like GPT or Claude to write a grounded answer. WavX Solutions builds this so replies stay accurate and cite your own content.

Which vector database is best for a RAG chatbot?

Popular choices include Pinecone, Weaviate, Qdrant and pgvector, chosen by scale, budget and hosting needs. WavX Solutions selects the vector DB that fits your data volume and keeps monthly costs sensible.

How is a RAG chatbot different from a normal chatbot?

A normal chatbot follows scripted rules or answers from the model's general training. A RAG chatbot answers from your specific documents, so it stays current and reduces hallucination. WavX Solutions grounds every answer in your knowledge base.

Want a chatbot that answers from your own content? Get a free quote from WavX Solutions and we will design a RAG architecture matched to your data and budget.