Company knowledge,
always up to date.
RAG lets AI assistants retrieve real information from company sources before generating a response. The result: fewer generic answers, greater precision, more context and more control.
AI that really knows your company.
Traditional AI models rely on pre-training data that is often generic or out of date. RAG addresses this limitation by retrieving the most relevant information from company sources before generating an answer.
With Tailor, RAG works within a private environment: documents, procedures, databases, repositories and knowledge bases are indexed, queried and used by AI assistants according to company-defined access rules.
databases
and repositories
Tailor
Four reasons to choose enterprise RAG.
Private RAG lets you use AI on company data while retaining control, traceability and security.
Up-to-date accuracy
AI retrieves the latest information before answering. Less outdated data, fewer generic responses.
Company context
Every response can be built on your documents, processes, customers, procedures and internal repositories.
Verifiable sources
Responses can be linked to original sources, making the process more transparent and controllable.
Private governance
Documents, embeddings, queries and answers remain within the controlled perimeter of company infrastructure.
From company sources to AI answers.
RAG turns company knowledge into a layer that Tailor assistants can query. Before answering, AI retrieves the most relevant context and uses it to produce more precise output.
Where RAG creates value.
RAG is useful wherever AI needs to answer using specific, up-to-date and controlled knowledge.
Want to use RAG on your documents?
Talk to our team about the sources, repositories, governance, access and RAG architecture best suited to your organisation.