RAG Development Services: AI That Answers From Your Own Data
Assistants, search and support bots that answer from your documents, products, tickets and databases, with sources cited and access rules respected. Built to be accurate, not just impressive in a demo.
- Answers grounded in your own content
- Citations back to the source document
- Role-based access to sensitive data
- Evaluation, guardrails and cost control
Rated 5.0 on Clutch & 4.8 on GoodFirms by verified clients in the USA, UK, Australia, Europe & India
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Continue on WhatsAppWhy retrieval-augmented generation beats a plain chatbot
- Internal knowledge assistant for staff
- Customer support bot on your help center
- Product and catalog Q&A for ecommerce
- Contract, policy and compliance search
- Sales enablement assistant for proposals
Our RAG development services connect a large language model to your own knowledge: manuals, policies, product catalogs, contracts, support tickets and database records. Instead of guessing, the model retrieves the most relevant passages first and writes its answer from them, with links back to the source.
That makes RAG the practical way to put AI on top of business data without training a model from scratch. It stays current as your documents change, it can respect who is allowed to see what, and you can measure how accurate it is before real users rely on it.
We design the whole pipeline: document ingestion and cleaning, chunking, embeddings, the vector database, retrieval and re-ranking, prompt design, evaluation and monitoring, then wrap it in the interface your team or customers will actually use.
What our RAG development services include
Every engagement starts with a free consultation and a written, fixed quote.
Knowledge ingestion
Connectors for PDFs, Word, web pages, Google Drive, SharePoint, Notion, Confluence, helpdesk tickets and databases, with cleaning and scheduled re-syncs.
Chunking and embeddings
Document splitting, metadata and embedding models chosen and tested for your content type, language and accuracy needs.
Retrieval and re-ranking
Hybrid keyword plus semantic search, filters and re-ranking so the model sees the right passages, not just similar ones.
Grounded answers with citations
Prompts and answer formats that cite sources, say when they do not know, and follow your tone and policies.
Access control
Per-user and per-team permissions so the assistant only retrieves documents the person asking is allowed to see.
Evaluation and monitoring
Test question sets, accuracy scoring, logging of answers and feedback, and alerts when quality or costs drift.
Want AI that answers from your own data?
Tell us what questions people ask and where the answers live. We will send a plan, timeline and fixed quote for a RAG prototype.
- 100% free
- No obligation
- Reply within 24 hours
What a well-built RAG system gives you
Faster answers
Staff and customers find the right answer in seconds instead of searching folders or waiting for an expert.
Fewer support tickets
Common questions are answered from your own help content, so your team handles only the cases that need a person.
Trustworthy output
Every answer links to its source, so people can check it and errors are easy to spot and fix.
Your data stays yours
Deploy with providers and hosting that meet your privacy needs, including private cloud and open-source models.
- OpenAI
- Anthropic Claude
- Google Gemini
- Llama
- LangChain
- LlamaIndex
- pgvector
- Pinecone
- Qdrant
- Weaviate
- Elasticsearch
- Python
- FastAPI
- AWS
From first call to launch, with no surprises.
Clear milestones, weekly demos and a fixed price agreed before any work starts.
Use-case workshop
We pick the questions worth answering, the sources needed and how success will be measured.
Data audit
We review your documents and systems, access rules and data quality, and plan ingestion.
Prototype
A working assistant on a sample of your data, tested against real questions within weeks.
Production build
Connectors, permissions, interface, evaluation suite, monitoring and cost limits.
Launch and improve
Roll-out to users, feedback review and regular tuning of retrieval and prompts.
Senior engineering, with the safeguards that make outsourcing safe.
7+ years of building and maintaining software for clients in the USA, UK, Europe, Australia and India. Recognised by Clutch as a top development company.
Pay per milestone
You never pay for work you have not seen and approved.
You own everything
Full source code, designs and accounts are transferred to you. No lock-in.
NDA before you share
We sign your NDA before you tell us about your idea. Your IP stays yours.
Full transparency
Weekly demos, a shared task board and direct access to your developers.
Your time zone
Overlapping hours with the USA, UK, Europe, Middle East and Australia.
Senior engineers
Your project is built by experienced developers, not trainees.
What clients say about working with Ravindra and the team.
Verified reviews from Clutch, GoodFirms, LinkedIn and long-term clients in the USA, UK, Australia, Europe and India.
RAG Development Services: frequently asked questions
Ask us on WhatsApp. A real person replies, usually within minutes during working hours.
Ask on WhatsAppWhat are RAG development services?
RAG development services build AI systems that retrieve relevant passages from your own documents and data before generating an answer. This keeps answers grounded in your content, lets them cite sources and avoids training a custom model. We handle ingestion, the vector database, retrieval, prompts, evaluation and the user interface.
How accurate will the answers be?
Accuracy depends on your content and how retrieval is tuned. We build a test set of real questions, measure answer quality before launch, and keep improving it with user feedback. The assistant is designed to say when it cannot find an answer rather than invent one.
Is our data safe?
Yes. We can use API providers that do not train on your data, private cloud deployments or open-source models hosted in your own environment. Access controls make sure users only retrieve documents they are allowed to see, and everything is logged.
How long does a RAG project take?
A focused prototype on a sample of your data usually takes 2 to 4 weeks. A production system with connectors, permissions, evaluation and monitoring typically takes 6 to 12 weeks depending on the number of sources and integrations.
What does it cost to run?
Running costs come from model usage, embeddings and hosting. We estimate them upfront from your expected traffic, choose models that fit the budget, cache common answers and set usage limits so costs stay predictable.
