Local LLMs
A local LLM and RAG on your corporate data
AI systems that answer from your internal documents, keep sensitive data inside the perimeter and give verifiable links to sources.
Stack and integrations
Qdrant PostgreSQL Mistral Qwen vLLM Docker
In short
- What we build
- A RAG system on corporate documents: answers with citations, no hosted LLM in the loop.
- Who for
- Companies with a thousand or more internal documents, where confidentiality and verifiability matter.
- Stack
- Qdrant with bge embeddings, local LLMs (Mistral, Qwen), vLLM, Docker.
- Timeline
- Pilot in three to six weeks, production in two to four months.
A good fit when
- → You have a large body of documents, regulations, manuals or correspondence
- → You need answers with sources rather than free generation
- → The data cannot go to public AI APIs
- → You need access roles and an audit trail of queries
What you get
- → Document indexing, chunking, embeddings and vector search
- → A RAG pipeline with source citation and hallucination control
- → On-premise or private cloud deployment
- → An API for integration with your portal, CRM, bot or helpdesk
Process
- 01 Data audit: formats, access rights, source quality
- 02 Pilot: two or three scenarios on a limited corpus
- 03 Production: monitoring, roles, index updates, documentation
Why not an off-the-shelf product
Custom development earns its place where your processes matter.
Ready-made SaaS is good for standard scenarios. But once the business logic depends on specific roles, documents, integrations, security or data, the cost of the workarounds quickly exceeds the cost of a proper architecture.
We start with discovery, separating what genuinely has to be built from what is cheaper to cover with an existing service. That is why the project ends up smaller, clearer and easier to run.
Related cases
Similar problems from the portfolio.
FAQ
Common questions
Do we need our own GPU server?
Not always. Retrieval and RAG often work in a hybrid setup; the GPU is needed when local generation is mandatory.
Which documents can be indexed?
PDF, DOCX, HTML, Markdown, knowledge bases, CRM exports and structured tables once they are prepared.
How does the knowledge base stay current?
We set up scheduled reindexing or event-driven import from the source, so answers do not go stale.
What does a local LLM with RAG cost?
A pilot on a limited corpus is the cheapest honest test. GPU infrastructure is bought or rented separately and is always a distinct line in the budget.
What are the timelines?
A pilot with two or three scenarios takes three to six weeks. A full production RAG with roles, auditing and index updates takes two to four months.
Which is more accurate, hosted or local?
Frontier hosted models are usually more accurate, especially on long answers. A local LLM wins on confidentiality and on cost at high volume.
How are hallucinations controlled?
RAG returns quotes from sources, unsupported answers are marked as 'no data', and critical operations require confirmation by an operator.
Can the local model be fine-tuned for our domain?
It can, but good RAG is usually the better investment: easier to update and it gives verifiable answers. We fine-tune when the task is mass generation of repetitive documents.
Next step
Let us go through your problem.
We will show you a possible architecture, the risks, the order of the budget and what an MVP could prove.
Discuss a project