RAG & LLM Integration

What this service does

RAG — retrieval-augmented generation — connects a language model to your own documents instead of letting it answer from general knowledge alone. When someone asks a question, the system first retrieves the relevant pages from your policies, manuals, price lists or knowledge base, then answers from those sources. The answer is grounded in what your business actually says.

That grounding is only half the work. Expert RLHF (reinforcement learning from human feedback) and evaluation test the system on real questions, edge cases and wrong answers, and keep improving it with human-reviewed feedback. The result is an assistant your team can trust with customer-facing and internal questions — because its answers can be checked, and its limits are known.

What’s included

• Document ingestion from policies, manuals, FAQs and knowledge bases
• Retrieval over your own sources, with citations to the source document
• Guardrails for out-of-scope, sensitive and high-risk questions
• Expert RLHF — human-reviewed feedback improving answers
• Evaluation suites for accuracy, hallucination and refusal behaviour
• Access controls so staff and customers only see what they should

Who it’s for

How we build it

A clear, low-risk path — you see the system working before you scale it.

What you receive

• Grounded Q&A over your own documents, with source citations
• Guardrail and access-control rules, documented and tested
• Evaluation report showing accuracy on real questions before launch
• Ongoing feedback loop — human-reviewed corrections

Frequently asked questions