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RAG

Enterprise RAG AI Knowledge Base Development

RAG retrieves enterprise documents first, then has a large language model compose answers from that material. It is not “training” another ChatGPT on your entire company file dump.

適合解決的問題

  • ・Lots of PDFs, Excel, and Word—finding one policy takes forever.
  • ・New hires repeatedly ask the same SOP, warranty, or product questions.
  • ・Want natural-language questions, but answers must come from company data and be traceable.

可以包含什麼

  • ・PDF, Word, Excel, FAQ, SOP, and product data
  • ・Document parsing, chunking, and embeddings
  • ・Vector retrieval with cited answers
  • ・Permissions: who can ask which document classes
  • ・Admin backend: update and retire outdated documents

怎麼做

  1. Documents

    Import and parse PDF, Office, or database content.

  2. Index

    Chunk, generate embeddings, store in PostgreSQL / pgvector or another vector store.

  3. Answer

    Retrieve relevant chunks, have the model answer, and show sources. Add reranking when needed.

適合誰、時間與費用

  • ・Companies with large internal document sets
  • ・Support or sales teams that need product / policy lookup
  • ・Teams that don’t want confidential files pasted into public chat tools

時程：Small-scope knowledge bases commonly take 4–10 weeks, depending on document quality, permissions, and admin needs.

費用：AI / RAG systems typically from NT$100,000–300,000, adjusted by data volume, permissions, and whether existing systems are integrated.

正式價格於需求確認後提供，以上為參考。

我們不會直接承諾的事

  • ・When documents are outdated or contradictory, AI can still answer wrong. Reduce hallucination with sources and update workflows—don’t promise zero error.
  • ・We won’t promise the system “automatically learns all company data” before the data scope is defined.

相關案例

  • Leaf AI Workspace 企業 AI 助理

    Leaf AI Workspace（leafflow-ai.vercel.app）：企業文件 RAG 問答、白名單工具查專案／帳款，以及通知前人工確認的公開示範。

常見問題

How is RAG different from general ChatGPT?

General ChatGPT answers from the model’s prior knowledge. RAG searches documents you provide, requires the model to answer from retrieved content, and cites sources. It can still misread a passage—so you must be able to open the original.

RAG or fine-tuning for enterprise knowledge?

Company policies, products, and SOPs change—start with RAG. Fine-tuning suits fixed tone or format, not a living knowledge base.

How are permissions handled?

Limit the searchable document set by login identity. Filter before answering so department A can’t retrieve department B’s internal files.

Have a similar system need?

Share your current workflow, existing systems, or the problem you want to solve. We first check technical fit, then decide on a PoC or a formal quote.

Free development assessment
RAG Development | Enterprise AI Knowledge Base & Document Q&A Systems