RAG & Knowledge Base
Create intelligent systems that understand and leverage your data to generate precise and contextual responses.
Why adopt RAG?
Maximum Precision
Answers based on your actual data with verifiable sources, no hallucinations
Dynamic Updates
Knowledge base always up-to-date, real-time document addition without retraining
Semantic Search
Find information by meaning and context, not just keywords
Unlimited Scalability
Handle millions of documents with constant response times
Security & Control
Private internal data, fine-grained access management and complete traceability
Contextual Intelligence
Deep context understanding for ultra-relevant responses
RAG architecture in 4 steps
Ingestion & Chunking
Collecting your documents, intelligent segmentation into chunks and contextual metadata extraction.
Vectorization
Creating semantic embeddings for each chunk and storage in an optimized vector database.
Intelligent Retrieval
Hybrid search (semantic + keywords) and contextual relevance ranking to find the best sources.
Augmented Generation
Synthesis of a precise response by the LLM based only on retrieved sources, with citations.
Custom RAG solutions
Document Q&A
Intelligent assistant capable of answering any question based on your technical documentation, manuals or guides
Semantic Search
Search engine that understands intent and context beyond simple keywords for relevant results
Documentation Assistant
Expert chatbot available 24/7 on your entire internal or customer document base
Knowledge Mining
Automatic extraction and structuring of hidden knowledge in your unstructured documents
Automated Technical Support
Assistant capable of diagnosing problems and proposing solutions based on your resolved ticket base
Intelligent Onboarding
Interactive integration guide answering new employee questions based on your HR documentation
Document Monitoring
Automatic surveillance and synthesis of large quantities of sector or regulatory documents
Compliance Assistant
Automatic compliance verification by querying your rules and regulations base
RAG Technology Stack
Frequently asked questions
What's the difference between RAG and a classic chatbot?
A classic chatbot generates responses based on its general training and can hallucinate. RAG first searches in YOUR documents for relevant information, then generates a response based only on these verifiable sources. It's more precise, factual and traceable.
What types of documents can be integrated?
All formats: PDF, Word, Excel, PowerPoint, HTML, Markdown, JSON, CSV, TXT, images (OCR), audio (transcription), videos (transcription). We also process structured sources: databases, APIs, CRM, internal wikis.
How many documents can the base handle?
Our solutions scale from a few hundred to several million documents. For example, we manage bases of 500K+ documents with response times < 2 seconds. Size doesn't impact performance thanks to vector indexing.
How do you guarantee data confidentiality?
Data stays in your infrastructure (on-premise or private cloud). We use encryption, user/group controlled access, and can implement RAG with local models (Llama, Mistral) for zero leakage to external APIs.
Transform your documents into intelligence
Let's create together your AI-augmented knowledge base for instant and precise answers.