AI answers based on your documents – not just on training data.
Retrieval-Augmented Generation (RAG) combines a language model with a targeted search of your own content. Instead of guessing or answering only from general knowledge, KAI first retrieves the relevant passages from your files, notes and connected sources and uses exactly those as the basis for its answer.
From document to source-grounded answer.
Capture documents
Files, notes, shared folders and other knowledge sources are stored in KAI and prepared for search.
Define access
Knowledge can be assigned to departments. Every request is answered only against the sources the requesting person has access to.
Find what matters
For every question, KAI searches specifically for the matching passages in the accessible documents.
Answer with sources
The answer is based on the passages found and names the underlying files. If the information is missing, KAI says so openly instead of speculating.
One place for the questions hidden in scattered files and folders.
Files & documents
Contracts, reports, manuals and other files are uploaded and made searchable.
Shared folders
Existing folder structures (e.g. network drives) can be connected as a knowledge source instead of maintaining content twice.
Notes
Personal notes complement the document base as an additional, individual knowledge source.
Why KAI relies on retrieval instead of model training.
Fine-tuning trains a model on a fixed snapshot of data – changes require retraining, and the training data becomes part of the model itself. RAG keeps model and knowledge separate: your documents remain your documents, and every change to them affects the next answer immediately – without retraining and without content permanently flowing into a model.
RAG at KAI in detail.
What is Retrieval-Augmented Generation (RAG)?
RAG is a method in which an AI, before answering, specifically searches your own documents and knowledge sources and uses the content it finds as the basis for its answer, instead of relying solely on generally trained knowledge.
How does RAG differ from fine-tuning?
Fine-tuning permanently changes a model using training data. RAG does not change the model; instead it supplies it at runtime with the relevant excerpts from current documents – new content is available immediately, without retraining.
How do I know what a KAI answer is based on?
KAI answers questions based on the retrieved document passages and names the underlying files. If KAI finds no matching information, it says so openly instead of inventing an answer.
Who may search which company documents?
Knowledge can be assigned to departments and access to it can be restricted. KAI answers questions only from the sources the requesting person actually has access to.
RAG is the knowledge base – Private AI governs where and how it is processed.
RAG answers the question of which knowledge flows into an answer. The question of where and with which models this processing takes place is answered by our Private AI model. And when an AI agent acts on this knowledge, the same governance rules apply to access, approval and logging.
Make your company knowledge usable – instead of searching for it.
Talk to us about your documents, departments and access requirements.