// SERVICE · LOCAL AI + RAG
AI that answers with your data. Without it leaving your company.
We connect a local LLM to your documents, manuals and systems. Your team asks in plain language; the model answers and cites the source.
WHAT IT IS
RAG, in plain English
Retrieval-Augmented Generation: the model searches your information before answering and shows you where the answer came from.
RETRIEVE
Searches your sources
For every question, it finds the relevant passages in your indexed documents.
AUGMENT
Gives the model context
It passes that context to an LLM running on your server, along with the original question.
GENERATE
Answers with a citation
It writes the answer in plain language and points to which document and page it came from.
HOW IT WORKS
From your documents to an answer
01
Ingestion
Manuals, PDFs, emails and your database, with role-based permissions
02
Indexing
Embeddings generated and stored on your server
03
Query
Internal chat, WhatsApp or straight inside your system
04
Answer with source
Traceable and verifiable: document, page, data point
PRIVACY
Local means local
Zero data out
Nothing travels to third-party APIs: not your documents, not your team’s questions, not the answers.
OPEN MODELS
Llama, Mistral, Qwen — open-weights running on your server or in a private cloud you control.
TRACEABLE
Every answer cites its source, so you can check it against the original document. If the data is not in your documents, the model says so.
DELIVERABLES
What the service includes
- ✓ Assessment of sources, permissions and role-based access
- ✓ Automatic ingestion pipeline — new documents get indexed on their own
- ✓ Internal chat + integration with WhatsApp or your management system
- ✓ Citations and source traceability in every answer
- ✓ Evaluation with real cases from your operation before going to production
- ✓ Team training and post-launch support
Does the model also need to do things in your format? That is fine-tuning.
See fine-tuning →