Create intelligent systems that understand and leverage your data to generate precise and contextual responses.
Answers based on your actual data with verifiable sources, no hallucinations
Knowledge base always up-to-date, real-time document addition without retraining
Find information by meaning and context, not just keywords
Handle millions of documents with constant response times
Private internal data, fine-grained access management and complete traceability
Deep context understanding for ultra-relevant responses
Collecting your documents, intelligent segmentation into chunks and contextual metadata extraction.
Creating semantic embeddings for each chunk and storage in an optimized vector database.
Hybrid search (semantic + keywords) and contextual relevance ranking to find the best sources.
Synthesis of a precise response by the LLM based only on retrieved sources, with citations.
Intelligent assistant capable of answering any question based on your technical documentation, manuals or guides
Search engine that understands intent and context beyond simple keywords for relevant results
Expert chatbot available 24/7 on your entire internal or customer document base
Automatic extraction and structuring of hidden knowledge in your unstructured documents
Assistant capable of diagnosing problems and proposing solutions based on your resolved ticket base
Interactive integration guide answering new employee questions based on your HR documentation
Automatic surveillance and synthesis of large quantities of sector or regulatory documents
Automatic compliance verification by querying your rules and regulations base
RAG is the technology that enables AI to answer questions by drawing on your enterprise documents — manuals, procedures, contracts, databases — rather than relying on its generic training knowledge.
RAG works in three stages. First, your documents are split into fragments and converted into numerical vectors (embeddings) stored in a vector database. When a user asks a question, the system searches for the most relevant fragments using semantic similarity — not exact keywords, but meaning. Finally, those fragments are provided to the language model as context to generate a precise, sourced answer. The major advantage: the model can only respond with information from your documents, eliminating hallucinations and ensuring answer reliability.
Traditional search (like an internal search engine) relies on exact keyword matching. If you search for 'refund procedure' but the document says 'return process,' you'll find nothing. RAG understands semantics: it knows these two expressions refer to the same concept. Moreover, instead of returning a list of documents, RAG synthesizes a complete answer with cited sources. It's the difference between searching through a library and having an expert who has read all your documents and answers your questions instantly.
Our RAG solution ingests all document types: PDF, Word, Excel, PowerPoint, web pages, emails, Zendesk tickets, Confluence articles, Notion pages, SQL databases, REST APIs. Documents are updated automatically — when you modify a procedure, the RAG reflects it immediately. We also handle multilingual documents: ask a question in English about a document in French, and RAG understands and responds in your language.
RAG adds value wherever teams search for information. Customer support: the chatbot accesses your entire product documentation to answer technical questions. Legal: attorneys query a contract database to find specific clauses. HR: employees get instant answers to administrative questions. Training: new hires access an intelligent knowledge base. Engineering: developers query internal documentation. Each use case reduces information search time by 60-80%.
Discover key concepts related to this solution
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.
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.
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.
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.
RAG strictly limits the model to information present in your documents. If the information doesn't exist in the knowledge base, the system says so clearly instead of making up an answer. Every response includes the sources used, enabling easy verification. This approach reduces hallucinations by over 90% compared to using an LLM on its own.
Updates are automatic. As soon as a document is added, modified, or deleted in your sources (SharePoint, Confluence, Google Drive, etc.), the system detects it and updates the embeddings in real time. You can also trigger manual syncs. A dashboard lets you monitor the knowledge base status and data freshness.
Yes. Our RAG implementation is natively multilingual. You can ingest documents in English, French, German, or any other language, and ask questions in the language of your choice. The system performs cross-lingual semantic search and generates the response in the language of the question.
There is no theoretical limit. Our deployments routinely handle knowledge bases of 10,000 to 100,000+ documents. The vector database is designed for sub-second searches even on very large volumes. Performance remains constant thanks to optimized vector indexing.
An embedding is a numerical representation of a text's meaning. Instead of comparing keywords, embeddings compare meanings. Two sentences that say the same thing with different words will have similar embeddings. This technology is what makes RAG's semantic search far more powerful than traditional keyword search.
Let's create together your AI-augmented knowledge base for instant and precise answers.