An AI model that only relies on its training data will eventually make something up, especially about your company specifically. We, Seven Web Tech, build RAG (Retrieval-Augmented Generation) systems that connect the model to your own documents, database or knowledge base, so it looks up the real answer before responding instead of guessing.
RAG stands for Retrieval-Augmented Generation, and in plain language it means the AI doesn't just answer from memory, it first searches your actual documents, policies or database for the relevant section, then writes its answer based on what it found there. That's the difference between a chatbot that occasionally invents a policy and one that quotes your real refund policy document correctly, word for word if needed.
We have built RAG systems for internal knowledge search, where staff ask a question and get pulled straight from company handbooks and SOPs, and for customer support bots that answer based on the actual current policy rather than what the model assumed. Bring us your documents and we'll show you, on a free call, what a working search over them looks like before you commit to anything.
Documents change, pricing updates, policies get revised, new products launch, so we build the retrieval layer to stay in sync with your source documents automatically. When you update a file, the system picks up the new version, so you're not stuck maintaining a separate copy of your knowledge base for the AI.
We connect your PDFs, policy documents, wikis or database records into a searchable index the AI model can pull answers from.
Responses are generated from the actual document retrieved, and we can configure it to reference the specific source it used.
Your team can ask plain-language questions and get answers pulled directly from internal handbooks, SOPs and past records.
We build support bots that answer customer questions using your actual, current policy documents instead of general assumptions.
When you update a source document, the system re-indexes it automatically, so answers stay current without manual rework.
Because answers are grounded in retrieved documents, the model is far less likely to invent details that aren't actually true.
We have built retrieval systems over different kinds of company data across India, from PDFs to internal databases, so we know how to structure a knowledge base that actually retrieves well.
We design the retrieval and indexing approach around how your documents are actually structured, instead of dumping everything into one generic search.
A RAG system keeps saving your team search time and support workload every day, so we build it to stay accurate as your documents keep growing.
We help smaller teams start with a focused RAG setup over their most-used documents, without needing a large data infrastructure first.
We keep refining the retrieval quality and answer accuracy as we learn how your team or customers actually phrase their questions.
Our team members follow a step-by-step process to build a RAG system. Here's the process
We start by understanding what documents or data you want the AI to search, and who will be using it, internal staff, customers, or both.
We map out how your documents should be indexed and how retrieval should work, and share the plan with you before development starts.
We build the indexing and retrieval pipeline over your documents and connect it to the language model that generates the final answer.
We test the system with real questions against your actual documents to check it retrieves the right section and answers accurately.
Once testing is done, we make the RAG system live, connected to wherever your team or customers need to access it.
We run additional checks comparing answers against source documents to confirm the system isn't drifting from what's actually written.
We stay available to keep the retrieval index in sync as you add, update or remove documents over time.
Learn about all the reasons why you should choose Seven Web Tech as your RAG development company in India
Every answer is retrieved from your actual documents first, so responses stay tied to what's really written, not general assumptions.
We index PDFs, wikis, spreadsheets and databases you already maintain, instead of asking you to rebuild your documentation.
When source documents change, the retrieval index updates too, so answers don't fall out of date.
Grounding responses in retrieved documents cuts down significantly on the model making things up.
The system is built to handle a growing document set, whether that's dozens of files today or thousands later.
We remain available after launch to adjust retrieval quality, add new document sources or fix anything that needs attention.
If you have an issue or question that requires immediate assistance, you can click the button below to chat live with a Customer Service representative.
We usually respond to new enquiries within a few business hours.
RAG stands for Retrieval-Augmented Generation. Instead of answering purely from what it was trained on, the AI first searches your actual documents or database for the relevant information, then writes its answer based on that. A regular chatbot without RAG just answers from general training and can get company-specific facts wrong.
Yes, we can configure it to reference the specific document or section it pulled the answer from, so your team or customers can verify it against the source if needed.
PDFs, Word documents, internal wikis, spreadsheets, database records, most formats your business already has. We help assess what you have and how to structure it for good retrieval.
The system re-indexes updated documents so it starts answering from the new version. You don't need to maintain a separate copy of your content just for the AI.
Yes. We set up access controls around who can query the system and where your documents are stored, and we explain the data flow clearly before we start building anything.
No. Once it's live, adding or updating documents is usually a simple process we hand over to your team, and we stay available for anything more involved.
It depends on how much document data needs indexing and how it's currently organised. A focused internal knowledge search setup usually takes a couple of weeks. Message us on WhatsApp with an idea of your document volume and we'll quote from there.
