RAG MVP Development: AI That Answers From Your Own Data

We build RAG (retrieval-augmented generation) MVPs that answer questions from your documents, knowledge base or product data, with citations users can check. Fixed price from $8,000, delivered in 4–6 weeks, deployed to your cloud with full IP transfer.

Book a Free RAG Scoping Call

✅ Working RAG product in 4–6 weeks

✅ Answers with source citations

✅ Accuracy measured on your real questions

✅ PDFs, docs, websites, help centers and databases

✅ Model-agnostic, deployed to your cloud

✅ 100% IP transfer, NDA before the first call

Quick Answer

A RAG MVP is the first working version of an AI product that answers questions using your own data. It retrieves the most relevant passages from your documents, then a language model writes an answer grounded in them, with citations.

At Codersarts, a RAG MVP costs $8,000–$20,000 and takes 4–6 weeks. The price depends on how many data sources you have, what format they are in, how accurate answers must be and whether different users need different access.

Every build is fixed price and includes the full application layer, an evaluation set, cost controls and full IP transfer.

Key Facts

Do You Need RAG, a Simple LLM or an AI Agent?

RAG vs fine-tuning

For most MVPs, RAG is the right first step. Fine-tuning comes later, only if RAG and good prompting can't reach the quality you need.

How Much Does a RAG MVP Cost?

What moves the price

What's Included in Every RAG MVP

• Data audit and ingestion pipeline for your sources and formats

• Chunking and embedding strategy tuned to your documents

• Vector database setup in your cloud

• Retrieval tuning, with hybrid search and re-ranking where needed

• Answers with source citations and links back to the original document

• Guardrails: the system says when an answer is not in your data

• Web app or embeddable widget with authentication

• Admin tools to add, update and remove content

• Evaluation set built from your real questions

• Usage metering and a cost dashboard

• Deployment, documentation and handover, plus 30 days of bug fixes

How Long Does It Take to Build a RAG MVP?

A Knowledge Assistant over clean documents compresses this to about 4 weeks. You see a working demo every week.

How Do You Know a RAG MVP Is Accurate?

We measure quality before launch instead of judging it from a demo.

• Test set of 50–200 real questions with expected answers

• Retrieval hit rate: did the system find the right passage?

• Answer correctness and faithfulness: no claims beyond the sources

• Citation accuracy: every answer links to where it came from

• Unanswerable handling: "I don't have that information" instead of a guess

• Re-run after every change to prompts, chunking or models

We agree the target in week 1, typically 85% or more correct answers on the test set.

RAG Use Cases We Build

How Much Does a RAG Product Cost to Run?

Running cost covers model usage, embeddings, the vector database and hosting. It scales with question volume.

Estimates assume moderate usage with caching and model routing enabled. Re-indexing large document sets adds a small embedding cost. We give you a project-specific cost model during scoping.

Data Privacy and Security

• Your data stays in your cloud: documents, embeddings and logs live in your accounts

• Permission-aware retrieval: users only get answers from documents they can access

• No training on your data: we use zero-retention or enterprise model options where available

• Encryption in transit and at rest, with audit logs

• Self-hosted model option for strict data residency

Who Owns the Code, and What Do You Receive at Handover?

You own everything. On the final milestone payment you receive:

• Full source code in your GitHub or GitLab, with commit history

• Deployed infrastructure in your own cloud account

• Architecture and API documentation

• Ingestion pipeline, prompts and evaluation sets

• Runbooks for deployment and monitoring

• A recorded handover session with your developers

• 30 days of post-launch bug fixes

No licence fees, no proprietary platform, no lock-in.

When You Don't Need RAG

• General knowledge is enough. A direct LLM integration is faster and cheaper.

• Your content is small. If it fits in a few pages, it can go straight into the prompt.

• The AI must take actions. You need an AI Agent MVP Development.

• Your documents contradict each other. Clean up the source content first, or the AI will repeat the confusion.

Frequently Asked Questions

How much does a RAG MVP cost?

$8,000–$12,000 for a knowledge assistant over one or two data sources, and $12,000–$20,000 for a product-grade RAG system with multiple sources, permissions and admin tools. Advanced builds with multi-tenant data or OCR are quoted after scoping, from $20,000. Pricing is fixed before development starts.

How long does it take to build a RAG MVP?

4–6 weeks. A knowledge assistant over clean documents takes about 4 weeks; a product RAG system with several sources and permissions takes 5–6 weeks.

Should we use RAG or fine-tuning?

For most products, RAG first. RAG adds your knowledge to a model, updates instantly when documents change and gives citations. Fine-tuning changes a model's style or format and is only worth it when prompting and RAG can't reach the quality you need.

What data sources can you use?

PDFs, Word and Google Docs, web pages, help centers, Notion and Confluence, SharePoint, databases and APIs. Scanned documents and tables need OCR, which we scope separately.

How accurate will the answers be?

We agree a target in week 1 and measure it on a test set of your real questions, typically aiming for 85% or more correct answers. The system cites its sources and says when an answer is not in your data instead of guessing.

Can users only see answers from documents they are allowed to access?

Yes. Retrieval is permission-aware, so each user only gets answers from documents their role allows. Multi-tenant products keep each customer's data isolated.

What does a RAG product cost to run?

Typically $100–$400 per month at pilot stage, covering model usage, embeddings, the vector database and hosting. Costs scale with question volume, and every build includes a cost dashboard.

Who owns the code and our data?

You do. Code, infrastructure and data stay in your accounts, with 100% IP transfer on final payment. We use model providers' zero-retention or enterprise options where available.

Build Your RAG MVP

Tell us what your users ask, where your content lives and how accurate answers need to be. We'll come back within 48 hours with an architecture, a fixed price and a timeline.

Book Your Free RAG Scoping Call →

Related: AI MVP Development · AI Agent MVP Development · AI Chatbot MVP Development · POC Development · Codersarts AI