AI & analytics
AI on your company’s real data: where to start
How to connect AI to your company’s real data — with permissions, context and privacy — to query in natural language and generate real value.
Read articleKey takeaways
- RAG combines a language model with retrieval of your own, up-to-date data.
- It lets AI answer with the company’s real information, not just its training.
- It reduces invented answers and improves reliability and traceability.
- It needs a prepared, governed data layer to work well.
- It does not require retraining the model with sensitive data.
One big problem with generic language models is that they answer with what they learned during training, which neither knows your business nor is up to date. Asking a generic model about last quarter’s sales is useless: it does not know them. RAG solves exactly that and has become the reference approach for applying generative AI to enterprise data.
What it is
RAG (Retrieval-Augmented Generation) is a technique that combines a language model with a system that retrieves relevant information from your own data sources at answer time. The model does not "invent": it relies on the retrieved data, anchoring the answer in your company’s reality.
How it works, simply
Question
Natural language
Retrieval
Find relevant datain your sources
Generation
Model answerswith that data
Answer
ReliableCitable source
- The user asks a question in natural language.
- The system retrieves the most relevant data fragments from the company’s sources.
- The language model generates the answer based on that data.
- The answer can cite the source, providing traceability.
Why it matters to the business
RAG makes AI reliable for real cases: it answers with up-to-date company data, reduces "hallucinations" (plausible but false answers) and lets you know where each answer comes from. That makes it suitable for querying internal policies, operational data or documentation, where an invented answer would be unacceptable.
What it needs to work
RAG is not magic: its quality depends directly on the quality and governance of the data it retrieves. It needs a prepared, up-to-date data layer with access control, so each user only gets answers based on data they are authorised to see — and it does not require retraining the model with sensitive data.
With RAG, AI stops inventing: every answer rests on a real, citable data point from your company.
In summary
RAG combines a language model with retrieval of your own data at answer time, so AI answers with real, up-to-date company information — not just its training. It reduces invented answers, adds traceability and needs no retraining on sensitive data. Its prerequisite: a prepared, governed data layer.
Sources & further reading
Frequently asked questions
Does RAG eliminate AI’s invented answers?
It significantly reduces them by grounding answers in real retrieved data, and lets you cite the source. It does not eliminate them entirely, but greatly improves reliability.
Do I need to train my own model to use RAG?
Not necessarily. RAG works by retrieving your data and passing it to an existing model at answer time, without retraining it on your information.
What is the essential requirement?
A prepared, up-to-date, governed data layer with access control, so answers are reliable and respect each user’s permissions.
What does RAG stand for?
Retrieval-Augmented Generation: it combines retrieving your own data with a model’s language generation.
What cases is it especially useful for?
Querying internal policies, operational data or documentation in natural language, where an invented answer would be unacceptable and you need to cite the source.
Does RAG respect access permissions?
Yes, if the data layer is well governed: each user only gets answers based on data they are authorised to see.
Turn this data into results
Tell us what you want to achieve. Data Layer connects, processes and delivers the result up and running, with no infrastructure for you to manage.