What is enterprise RAG?
RAG (retrieval-augmented generation) is a technique in which a language model answers using the company's own documents and data, retrieved at query time, instead of relying only on its training. Metatech Perú implements enterprise RAG with curated sources, per-user access control, citations to source documents and continuous evaluation of answer quality.
Who it is for
- Areas that build reports by hand from several systems and spreadsheets.
- Service, legal, operations or HR teams that rely on scattered manuals and policies.
- Organizations that receive large volumes of documents and process them manually.
The problem it solves
- Figures that do not match across areas because each one calculates from its own source.
- Hours spent looking for a procedure, a contract or the current version of a document.
- AI assistants that confidently answer with outdated or made-up information.
- Invoices, orders, contracts or forms that someone must read and key in one by one.
How it works
Define what must be answered
We start from real business questions and decisions. That determines which data and documents matter and how accurate they must be.
Curate the sources
We identify current versions, remove duplicates and assign an owner to each source. A knowledge base is only as good as its documents.
Build retrieval and validation
We implement search, source citations and permissions. For transactional documents, we validate what is extracted against systems of record.
Evaluate continuously
We measure quality with reference question sets and review unsupported answers. Content is updated through a defined process.
What we deliver
- An inventory of data sources and the source of truth for each indicator.
- A data model and consolidation pipelines with quality controls.
- Automated reports and dashboards built on agreed definitions.
- A knowledge base for RAG: document selection, cleansing, chunking and indexing.
- Access control that respects each user's permissions on each document.
- Document intelligence flows: extraction, validation against systems and human review of exceptions.
What it integrates
When it makes sense
It makes sense when
- The same questions are answered many times and the answer is written in some document.
- Documents have a recognizable structure and steady volume.
- Someone can confirm which version of each piece of content is current.
- Decisions need data from more than one system.
Another option is better when
- The knowledge is not written anywhere: it has to be documented first.
- Absolute accuracy without human review is required for high-impact decisions.
- Volume is so low that manual search is enough.
Typical scenarios
Internal policy assistant
Employees ask about policies, procedures and manuals in natural language and receive the answer with a link to the current document, according to their access profile.
Accounts payable
Incoming invoices are read automatically and matched against the purchase order and goods receipt in the ERP; only discrepancies go to a person.
Unified indicators
Sales, operations and finance look at the same indicators, calculated once from agreed sources, instead of reconciling versions at every close.