Solution

LLM Integration & RAG

AI assistants grounded in your company's knowledge.

Your company's knowledge lives in PDFs, sheets, old emails, and people's heads. LLM integration with RAG (retrieval-augmented generation) turns that scattered knowledge into assistants that answer accurately — always grounded in your documents, with citations you can verify.

We obsess over the unglamorous part: chunking, retrieval quality, and evaluation. That's what separates an assistant your team trusts from one they stop using after a week.

Concrete use cases

Internal knowledge chatbot

New hires and support staff ask questions in plain language and get answers pulled from your SOPs, manuals, and past tickets — with source citations. Onboarding time drops, and senior staff stop answering the same questions repeatedly.

Support-ticket drafting assistant

The assistant reads an incoming support ticket, pulls relevant history and docs, and drafts a response for your agent to review and send. Resolution times fall without losing the human touch.

Policy & price lookup assistant

Sales and ops teams get instant, accurate answers on pricing, policies, and stock from a single chat interface instead of hunting through spreadsheets and PDFs.

What's included

  • Private knowledge base grounded on your documents
  • Chat and email assistants with cited answers
  • Evaluation harness measuring answer accuracy
  • Cost-optimised model routing and caching

LLM Integration & RAG — FAQs

Yes — everything runs inside your accounts and access controls. Documents stay in your storage; the AI only reads what it's permitted to see, and nothing trains public models.

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Ready to automate with llm integration & rag?

Book a free consultation. We'll map your most repetitive workflow and show you what an AI pilot could look like.