Knowledge bases and document search
Search and answers over the company’s internal documents: policies, manuals, contracts and the knowledge base. Each answer rests on your sources and shows where the information came from.
Who it is for
Companies where staff lose time searching folders, wikis and chats, support answers the same questions again and again, and knowledge lives in a few people’s heads.
What the client gets
Working search and answers over your documents, with links to sources — in a portal, a messenger or a product. We hand over the source code, a description of how documents are loaded and updated, and a set of examples for checking quality.
Document preparation
Parsing PDFs, office files and knowledge-base pages, splitting them into passages and keeping them current as they change.
Semantic search
Search by meaning rather than matching words: a vector database and ranking tuned to your documents.
Answers with sources
The answer is assembled from the passages found and cites them, so it can be checked.
Access rights
Each employee gets answers only from the documents they are allowed to see.
Quality checks
Questions with reference answers, used to measure search and answers after every change.
Questions
How is this different from ordinary search?
Ordinary search finds words; semantic search finds meaning: “how do I request leave” finds the policy even if it uses other words. The model assembles the answer from the passages found and shows where it came from.
What if the documents do not contain the answer?
The system should say there is no answer rather than invent one. We test this with dedicated examples in the evaluation set.
Can we avoid sending documents to external services?
Yes. The vector database and the model can run in your infrastructure; we choose the mode from your data requirements before work starts.