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RAG and LLM development services grounded in your own data.

We build retrieval-augmented generation systems and LLM features that answer from your documents, cite their sources and plug into the products you already run.

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What are RAG and LLM development services?

RAG (retrieval-augmented generation) lets a large language model answer using your own documents, retrieved at question time, instead of relying only on what it was trained on. GreenMindAI builds RAG pipelines, vector search and LLM integrations using OpenAI, Gemini and other providers, with Python, FastAPI and LangChain, for clients worldwide.

How RAG and LLM projects work

RAG or LLM feature

Grounded in your documents
Vector search and retrieval pipeline
Evaluation on real questions
Dedicated relationship manager

Scope depends on data volume, integrations and accuracy needs, agreed after a discovery call.

What you get

Document ingestion, chunking and vector database setup
Chat and search over internal or customer-facing knowledge
LLM features inside your web or mobile product
Tool use and workflow automation connected to your systems
Evaluation on real questions before launch
Logging and monitoring in production

How we deliver

Define the questions it must answer
Prepare and index the data
Build and evaluate retrieval
Integrate into your product
Monitor and improve

Why GreenMindAI

We evaluate on your real questions before anything ships.
Honest guidance on RAG versus fine-tuning for your use case.
Model-agnostic: OpenAI, Gemini and others, chosen per use case and budget.
Every plan includes a dedicated relationship manager who handles your brand end to end, so you always have one person to talk to.

Related services.

LLM workflows, RAG, automations

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iOS, Android, realtime experiences

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Further reading.

Common questions.

What is RAG?

Retrieval-augmented generation retrieves relevant passages from your documents and gives them to a language model so its answer is grounded in your data.

RAG or fine-tuning?

RAG is usually the better start when answers depend on changing company documents. Fine-tuning suits changing style or behaviour. See our guide comparing both.

Which models do you use?

OpenAI, Gemini and other providers, chosen per use case and budget.

Which vector databases do you work with?

We choose the vector store to suit your data volume, hosting and budget.

How do you reduce wrong answers?

By grounding answers in retrieved sources, evaluating on real questions before launch, and keeping a human in the loop for sensitive actions.

How is a RAG project scoped?

By data and integrations. We agree the scope after a short discovery call.

Do you work with clients outside India?

Yes. Delivery is remote-first with overlapping hours for your time zone.