RAG development
RAG development services: AI that answers from your own data.
Clarigo Infotech builds retrieval-augmented generation (RAG) systems: AI search and assistants that find the right information in your documents, databases and knowledge base, then answer with sources. RAG keeps answers accurate and current without retraining a model, and respects who is allowed to see what.
10+
Years of delivery
600+
Projects delivered
20+
Countries served
25-30
Days to a live MVP
Get a free consultation
Tell us what you need. A senior engineer replies within one working day.
What we build with RAG
Chat with your documents
Ask questions across PDFs, policies, manuals and contracts and get answers with citations.
Enterprise knowledge assistants
One place for staff to find answers across Google Drive, SharePoint, Confluence and more.
Customer support answers
Grounded answers for support teams and chatbots, drawn from your help centre and product data.
Search over structured data
Combine document search with SQL and APIs so answers include live numbers.
Permission-aware retrieval
Users only get answers from content they are allowed to see.
Evaluation and tuning
Test sets that measure retrieval and answer quality, and tuning until they pass.
How we approach RAG development
Why RAG instead of fine-tuning
Fine-tuning changes how a model writes; RAG changes what it knows at the moment it answers. For most business questions RAG is cheaper, easier to update and easier to audit, because every answer points to its source.
Getting retrieval right
Quality depends on how documents are cleaned, split, embedded and searched. We use hybrid search (keyword plus vector), re-ranking and metadata filters, and test retrieval separately from the final answer.
Keeping it current and secure
Connectors keep the index in sync as documents change, and access rules from your source systems are applied at query time so nobody sees what they shouldn't.
Measured, not guessed
We build a test set of real questions with expected answers and track accuracy, citation quality and 'I don't know' rates, so you can see improvement and catch regressions.
How it works
Week 1: discovery and design
We agree the use case, success measures and data, and show you a clickable design.
Weeks 2-3: build and integrate
We build, connect it to your systems and data, and demo working software every week.
Week 4: test and launch
We test with real cases, add monitoring and guardrails, then go live.
After launch: improve and scale
The same engineers keep tuning quality, cost and features as usage grows.
See what we've built
Try it yourself, free
Free browser tools from our team. No sign-up needed.
Questions we hear most
What is RAG (retrieval-augmented generation)?+
RAG is a way of building AI assistants that first search your own documents and data for relevant information, then use a language model to answer from what was found, usually with citations.
How is RAG different from a normal chatbot?+
A normal chatbot answers from what the model learned in training. A RAG assistant answers from your current, private information, so it is more accurate for your business and can show its sources.
Which data sources can you connect?+
PDFs and Office files, Google Drive, SharePoint, Confluence, Notion, websites, help centres, databases and APIs. We add connectors so the index stays up to date.
Will people see documents they shouldn't?+
No. We apply the same permissions as your source systems at query time, so each user only gets answers from content they can access.
How long does a RAG project take?+
A focused RAG assistant over one set of sources typically goes live in 25 to 30 days, including a test set to measure quality.