AI search in company data

Have internal documents, manuals, policies or databases and people search them with keywords? AI search (RAG – retrieval augmented generation) finds relevant passages and can answer in natural language.

The problem: full-text isn’t enough and nobody wants to read hundreds of pages

Full-text search returns pages where a word appears – without understanding context. Staff or customers need an answer to a specific question, not a list of files. Browsing large documents is time-consuming.

How AI search helps

• Semantic search – query in natural language, system finds passages by meaning, not just words.
• RAG (Retrieval Augmented Generation) – find relevant documents + GPT composes a short answer with source links.
• Knowledge base – internal FAQ, manuals and policies in one interface with queries in your language.
• Security – data can stay with you, embeddings and index in private cloud or on-premise.

Real-world example

A company has dozens of internal policies and manuals in PDF. Staff often don’t know where to find answers. AI search: user asks a question (e.g. “How do I report holiday?”), system searches documents via embeddings, selects relevant sections and GPT composes an answer with a link to the document and section. Search time went from minutes to seconds.

Technologies

• OpenAI – Embeddings API for text vectors, GPT to compose the answer from found passages.
• LangChain – chain: load query → search index → call GPT with context → return answer.
• Pinecone, Weaviate – storing document embeddings and fast retrieval of passages closest to the query.

No-obligation inquiry – AI search in your data
Email: rasovsky.mar@gmail.com · Phone +420 605 219 155

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