Enterprise RAG systems and chatbots
RAG — Retrieval-Augmented Generation — is the technique that makes a language model answer from what is yours: your manuals, contracts, support history, product base. Instead of trusting what the model learned on the internet, the system searches your material and answers from it.
Done well, it's the difference between an assistant that cites its source and one that gives a convincing guess. Done badly, it's a generator of wrong answers that look certain.
When it makes sense
Knowledge scattered across documents
Manuals, internal policy, contracts, procedures. Information that exists, but nobody finds when they need it.
Support that queries the same base
A team answering questions whose answer is always in the same place — and every new hire takes months to learn where.
A catalogue too large to memorize
Thousands of products, codes or specs consulted all day long.
Slow onboarding
New people spending weeks learning where things live inside the company.
How we do it
- 01
We prepare the base before indexing
This step decides the outcome, and almost everyone skips it. Badly chunked documents produce bad answers no matter how good the model is. We split by unit of meaning, not by character count.
- 02
We index with metadata
Every chunk carries its origin, date and permission. That allows filtering by who can see what, and citing the source in the answer.
- 03
We build hybrid search
Semantic search alone fails on codes, numbers and proper names. We combine it with literal search, which is exactly where semantics breaks.
- 04
We tie the answer to the source
The system answers citing where it got the information. When it doesn't find enough basis, it says so instead of filling the gap with invention.
What you get
- A RAG system in production over your base
- Answers with source citation
- Per-document permission control
- A WhatsApp interface, a website widget, or both
- A process for updating when the base changes
Where we've applied it
Toreply.me puts a conversation layer on top of any legacy system: the company registers the APIs it already has and starts querying its own data over WhatsApp or an embedded widget, with answers coming from the real system — not from a stale summary.
Questions about RAG and chatbots
Do my documents go into the model?
No. In RAG the content stays in your base and is queried on each question; it is not used to train any model. When the data is sensitive, the whole system can run on your infrastructure, with a model you host.
RAG or fine-tuning — which one?
RAG is for knowledge that changes: prices, procedures, catalogues, regulations. Fine-tuning teaches format and style of response, not facts. In practice, almost every enterprise case that reaches us asking for fine-tuning is better solved with RAG, because the problem is access to current information, not model behaviour.
What happens when the system doesn't know?
It has to say so, and that's a design decision. We set a minimum relevance threshold: below it, the system replies that it didn't find enough basis and, if you want, routes to a person. An assistant that never admits ignorance is one nobody trusts after the third wrong answer.