To find out if RAG is the exact requirement for your product
RAG is powerful but not the right solution for every chatbot requirement. CoreFragment helps you assess your use case — document volume, query types, privacy requirements, response accuracy expectations and recommends the right architecture. Sometimes RAG is the answer. Sometimes a fine-tuned model or a hybrid approach works better. We give you an honest evaluation, not a sales pitch.
Help to deploy on-premise RAG solutions
If your documents contain confidential business data, patient information, legal content, or anything that cannot go to a third-party AI API, CoreFragment helps you deploy a fully on-premise RAG system using locally hosted LLMs. Your documents stay on your servers. Your queries never leave your network. You still get the full power of AI-driven document search.
Manage chatbot hallucinations
Hallucination is the biggest risk in AI chatbots for business use. CoreFragment builds RAG systems with retrieval confidence thresholds, fallback responses for low-confidence queries, and source citation features, so your chatbot only answers when it has reliable context, and tells users honestly when it does not.
Build architecture for pilot and scaling both levels
Whether you are starting with 50 documents or planning for 5,000, CoreFragment builds your RAG system on a vector database architecture and cloud infrastructure that scales without requiring a redesign. You start lean, validate with real users, and expand confidently knowing the foundation supports where you are going.
Automate your large document library to ease access of people
If your team or your customers spend time searching through manuals, reports, policies, or product documents for answers — we can help you build a RAG chatbot that does that searching for them. You upload the documents. Your users ask questions in plain language. The chatbot finds the right answer from the right document in seconds.
