RAG architecture: AI over your company's documentation
RAG (retrieval-augmented generation) is an architecture that connects a language model to your company's actual documentation. Before answering, the system retrieves the relevant fragments from your documents and answers only from that basis, citing where each fact came from. If the information isn't there, it says so.
What this service covers
The problem it solves
A company's knowledge lives scattered across manuals, contracts, procedures, presentations, and email. Finding a fact requires knowing which file holds it and who has it. RAG turns all that material into something you query by asking, and returns the answer with its source.
How it works, step by step
- Ingestion: documents are loaded (PDF, Word, presentations, internal pages, databases).
- Chunking: each document is split into fragments that stand on their own.
- Embeddings: each fragment becomes a vector representing its meaning.
- Vector database: vectors are indexed so they can be searched by semantic similarity.
- Retrieval: given a question, the system pulls the most relevant fragments.
- Reranking: results are reordered by actual relevance, not just mathematical proximity.
- Generation: the model writes the answer using those fragments and cites their origin.
Why it doesn't make things up
A language model on its own answers from what it learned in training, and fills in the gaps when it doesn't know. That is where hallucinations come from. In a RAG architecture the model answers only from fragments retrieved from your documentation, every answer shows the document and section it came from, and when there isn't enough material the system says it can't find it instead of improvising. That traceability is verifiable: anyone can open the source and check.
Common use cases
- Query manuals and technical documentation without depending on the specialist who knows them.
- Answer questions about internal procedures and company policy.
- Find clauses and conditions inside contracts and tender documents.
- Support the service team with real, current information.
- Onboard new people: they ask the system instead of interrupting the team.
- Query in-house research, reports, and presentations from previous years.
Keeping the knowledge current
Documentation changes. The ingestion pipeline is automated so new or modified documents are reindexed without redoing anything: the base updates incrementally and answers reflect the current version, not the one from the day it was implemented.
Security and access control
- Documents stay on the infrastructure you define, your own or your cloud.
- Per-user and per-area permissions: each person queries only what they may see.
- Auditable log of queries and answers.
- Your documentation is not used to train third-party models.
RAG, fine-tuning, and a generic model: which one fits
All three are ways to make an AI answer within a domain, but they solve different things. Choosing wrong makes the project expensive or impossible to maintain.
| Criterion | Generic model | RAG | Fine-tuning |
|---|---|---|---|
| Answers from your documents | No | Yes | Partly |
| Cites the source of each fact | No | Yes | No |
| Updates when a document changes | Not applicable | Yes, on reindex | Requires retraining |
| Setup cost | Low | Medium | High |
| Cost of keeping knowledge current | Not applicable | Low | High |
| Risk of invented answers | High | Low | Medium |
| When it fits | General writing tasks | Proprietary knowledge that changes | Very specific style or format |
In practice, most enterprise projects are solved with RAG. Fine-tuning is justified when what the model needs to learn is a way of answering, not a body of information.
Implementation process
- 1
Diagnostic
We analyze your process and tell you what to automate, how, and what return to expect.
- 2
Implementation
We build the solution connected to your systems, with working deliveries and real tests.
- 3
Operation and improvement
We leave it running, train your team, and measure results.
The full detail, with deliverables per stage, is in how we work.
Frequently asked questions
Which document formats are supported?
PDF, Word, presentations, spreadsheets, internal web pages, and plain text. Content can also be ingested from databases and document management systems via API.
How do I know the answer is correct?
Every answer includes a reference to the document and section the information came from, so you can open the source and verify it. That is the core difference from a generic chatbot.
What if the information isn't in the documentation?
The system says it can't find it. It is configured not to fall back on the model's general knowledge when there is no support in the loaded material.
How many documents make it worthwhile?
There is no hard minimum: what matters is how often the material is consulted and what it costs not to find the information. A small set of heavily used manuals justifies the project as much as an archive of thousands of documents.
Does our documentation leave the company?
That depends on the design we agree on. The architecture allows the entire pipeline to be deployed inside your own infrastructure or cloud, and in every case the material is not used to train third-party models.
Can we limit who queries what?
Yes. Permissions are set per user and per area, so search only retrieves fragments from documents that person is authorized to see.
How is this different from an internal search engine?
A search engine returns a list of documents where the answer might be. RAG returns the written answer, citing the document it was drawn from.
How long does an implementation take?
A focused pipeline over a defined set of documents can be running in weeks. Projects integrating several sources, per-area permissions, and automatic updates take more stages. The diagnosis includes a concrete estimate.
Is your team losing time searching for internal information?
Tell us which documentation they consult every day and we'll assess whether a RAG architecture solves it.