RAG & LLM
Answers with sources, from your documents.
Retrieval-Augmented Generation: the language model answers only from your documents, citing where each piece of information comes from. Ingestion pipelines for sensitive material too — invoices, technical documentation, contracts — with local deployment when privacy demands it.
In practice
Ingestion that survives the real world
Scanned PDFs, invoices, drawings, e-mails, ERP records: parsing, OCR where needed, structure-aware chunking and metadata to filter by customer, year, department.
Retrieval with citations
Embeddings, hybrid search (semantic + keyword), re-ranking: every answer shows the passages it comes from. If the source is missing, the system says so.
Agents and tools
When a question requires an action — querying the ERP, calculating, opening a ticket — the LLM uses controlled tools, with role-based permissions.
Privacy by design
Local deployment with open-weight models when data cannot leave; encryption, access logs, continuous evaluation of quality and hallucinations.
Stack and technologies
- LangChain
- Hugging Face
- PyTorch
- Vector database
- Python
- Docker
Project
DeliveredSensitive material, local deployment
A RAG pipeline built with LangChain on a local installation: ingestion of confidential material — invoices and technical documentation included — retrieval with source citations and natural-language answers, without a single document leaving the company.
- Framework
- LangChain
- Data
- invoices, technical documentation
- Deployment
- local
- Output
- answers with cited sources
How we work
Four steps, always the same
- 01
Inventory of sources and access permissions
- 02
Ingestion and indexing pipeline, with retrieval quality tests
- 03
LLM integration (local or cloud), guardrails and citations
- 04
Evaluation with real questions, release, monitoring
Frequently asked questions
Does the model make things up?
A well-built RAG drastically reduces hallucinations: the model answers only from retrieved passages and cites them; when the information is missing, it says so. We measure the rate of unsupported answers before release.
Do documents end up in the cloud?
Only if you want them to. For sensitive material we deploy everything locally, with open-weight models served on your GPUs.
How long does it take?
A prototype on a real knowledge base typically takes 4–6 weeks; full integration with permissions, existing systems and monitoring, 3–6 months.
Related capabilities
All AI capabilities →Get started
Ready to transform your business?
Contact us for a free consultation and discover how we can help you reach your goals.