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RAG Implementation for Business – AI on Your Own Documents

AI assistant that answers questions using only your internal documents—contracts, policies, manuals. Every answer cited, zero hallucinations.

Key takeaways

AI assistant that answers questions using only your internal documents—contracts, policies, manuals. Every answer cited, zero hallucinations.

  • ›Answers grounded exclusively in your documents—the AI cannot fabricate information that isn't present in your actual knowledge base.
  • ›Source citation for every answer—users see exactly which document and paragraph the answer came from, enabling fast verification.
  • ›Handles diverse document formats natively: PDF, DOCX, Notion, Confluence, Google Docs, and plain text out of the box.

Price: from €2,000

SERVICE DETAILS

I build Retrieval-Augmented Generation (RAG) systems that give your team an AI assistant grounded exclusively in your proprietary documents. Upload contracts, policies, product manuals, or internal wikis—the system retrieves the most relevant passages and feeds them to the LLM, producing precise answers with exact source citations. No general knowledge hallucinations, and no data leaving your infrastructure if self-hosted.

› INVESTMENT:

from €2,000
const module = new ExecutionProtocol();

// Initializing rag-implementations...
› Loading dependencies... OK
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› Ready for deployment... AWAITING_COMMAND

Key Benefits

Answers grounded exclusively in your documents—the AI cannot fabricate information that isn't present in your actual knowledge base.

Source citation for every answer—users see exactly which document and paragraph the answer came from, enabling fast verification.

Handles diverse document formats natively: PDF, DOCX, Notion, Confluence, Google Docs, and plain text out of the box.

Access control layer—different user roles query only the documents they're authorized to see, enforced at the vector search level.

Self-hostable architecture—your documents and queries never leave your servers; compliant with GDPR, HIPAA, and enterprise data governance requirements.

The Process

1

Document Inventory & Chunking Strategy

I inventory your documents and choose the optimal chunking strategy—by section, by paragraph, or by semantic block—for your specific content type and query patterns.

2

Vector Store Setup

I configure the vector database (Pinecone, Weaviate, or pgvector), define metadata schemas for filtering, and index all documents with quality embeddings.

3

Retrieval & Generation Pipeline

I build the query pipeline: embed user question → vector search → rerank results → inject context into LLM prompt → return answer with source citations.

4

UI, Access Control & Launch

I build or integrate the chat interface, implement authentication and document-level access control, test accuracy on 100+ representative questions, and deploy.

RAG on company docs vs fine-tuning vs generic ChatGPT

CriterionRAG on company docsModel fine-tuningGeneric ChatGPT
Answer accuracyHigh on internal knowledgeHigh once trainedLow on private data
Source citationsYes, every answer citedNo native citationsNo
Hallucination riskLow — grounded in your docsMediumHigh on domain facts
Update effortRe-index changed documentsRetraining requiredNot applicable
Setup costModerate one-time buildHigher — training and data prepLowest, off the shelf
Data privacySelf-hostable, GDPR-friendlyDepends on training setupSent to a third party
Best forQ&A over evolving documentsFixed style or task behaviourGeneral, non-sensitive use

Frequently Asked Questions

How accurate is RAG compared to a standard ChatGPT?

On domain-specific questions, RAG accuracy is dramatically higher because the LLM sees your exact documents instead of relying on general training data. Accuracy typically improves from 40–60% to 85–95% for internal knowledge queries.

Can it handle hundreds of thousands of documents?

Yes. Vector databases like Weaviate and Pinecone scale to hundreds of millions of vectors. Search latency stays under 100ms even at that scale with proper index configuration.

What happens when my documents are updated?

I build an incremental indexing pipeline. Updated or new documents trigger a re-embedding job that updates only the changed vectors, keeping the knowledge base current without a full re-index.

How does RAG eliminate AI hallucinations in business applications?

Eliminating AI hallucinations in business is RAG's primary value. Instead of generating answers from general training knowledge, the vector search (Pinecone, LangChain) first retrieves specific passages from your documents, then the LLM formulates an answer based only on that retrieved context. Every answer is source-cited — the model physically cannot invent facts not present in your knowledge base.

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BIAŁYSTOK, PL
+48 732 022 086pawel.wiszniewski95@gmail.com