Part of RAG Systems

Private company knowledge chatbots

Ask questions across policies, wikis, contracts and tickets and get cited answers respecting user permissions.

Book a free AI consultation Back to RAG Systems

In one paragraph

Private company knowledge chatbots: Ask questions across policies, wikis, contracts and tickets and get cited answers respecting user permissions. Retrieval-Augmented Generation (RAG) connects a language model to your private documents, wikis, tickets and databases so it answers with citations instead of guessing. We build private knowledge chatbots, PDF chat, enterprise and semantic search, embedding pipelines and vector databases for organisations that need accurate, access-controlled answers over their own information.

How private company knowledge chatbots fits into rag systems

Support knowledge assistant
Agents and customers get accurate answers from the help centre and internal notes, with links to the source.
Policy and compliance Q&A
Staff ask about policies, procedures and regulations and get the current answer with the clause cited.
Contract and document review
Find clauses, compare versions and summarise obligations across thousands of documents.
Engineering knowledge search
Search code docs, runbooks, tickets and Slack in one place and get answers instead of links.

How we deliver

1
Content and access audit
Inventory sources, formats, owners and permissions; decide what belongs in the index and who may see it.
2
Retrieval design
Chunking, embeddings, metadata, hybrid search and reranking chosen and tested on your documents.
3
Ingestion pipeline
Connectors and refresh jobs so the index tracks the source of truth without manual uploads.
4
Answer layer and evaluation
Prompting, citations, refusal behaviour and a scored test set built from real questions.
5
Rollout and governance
Launch to a pilot group, monitor unanswered questions and quality, and set content ownership rules.

Frequently asked

RAG retrieves relevant passages from your documents at question time and gives them to the model, so answers are current, cited and access-controlled. Fine-tuning changes how a model behaves, not what it knows, and cannot keep up with changing content. For knowledge questions RAG is the right tool; the two are sometimes combined.

We store permission metadata with every chunk and filter retrieval by the requesting user, so nobody sees an answer built from a document they cannot open. Permissions are synced from the source system (SharePoint, Drive, Confluence) rather than copied once.

That depends mostly on retrieval quality and content quality. We build a test set of real questions with expected sources, measure retrieval hit rate and answer correctness, and tune chunking, embeddings and reranking until the numbers are good. We report those numbers rather than promising percentages up front.

For most teams pgvector in an existing Postgres is enough and simplest to operate. Pinecone or Weaviate suit larger scale and managed hosting; Qdrant and Milvus are strong self-hosted options; ChromaDB and FAISS fit prototypes and local use. We recommend based on scale, hosting and existing skills.