Your documents. One AI. Zero hallucination.
RAG connects your knowledge bases to generative AI: every answer is grounded in your official data, with the exact source and relevance score.
The AI that knows what it says.
A standard LLM generates plausible responses - not necessarily true ones. RAG reverses this logic: before generating, the AI retrieves the exact passages from your documents that answer the question.
Result: every response is traced, sourced, and verifiable. Your support teams, customers, and internal tools get reliable information - not hallucinations.
Index everything. Query anything.
Document ingestion
PDF, Notion, Confluence, Salesforce, SharePoint - intelligent chunking, text-3-large embeddings, indexing in your vector DB (Pinecone, pgvector, Qdrant).
Semantic search
Beyond keywords: our engine understands the meaning of every query and returns the most relevant passages with their cosine similarity scores.
Knowledge base chatbot
Support chatbot, internal assistant or customer FAQ - connected to your RAG knowledge base with source citations and automatic human escalation.
Auto-generated smart FAQ
The system identifies the most frequent questions in your tickets and automatically generates answers from your official documents.
Product sheet enrichment
Connect your product sheets to customer reviews, technical documentation, competitor comparisons and support data for complete answers.
Analytics & continuous improvement
RAGAS dashboard: precision, recall, faithfulness. Automatic feedback loop to re-rank sources and continuously improve the knowledge base.
Every query traced. Every source cited.
Our RAG monitoring interface displays in real time all queries, retrieved sources, relevance scores, and quality metrics. No answer without proof.
- Query log with sources and real-time scores
- RAGAS metrics (faithfulness, precision, recall)
- Automatic alerts on quality drops
- User feedback integrated into re-ranking
- Export of unanswered queries for improvement
From raw document to production RAG in 3 days.
Audit & architecture
Inventory of your document sources, vector DB selection, chunking schema definition and embedding strategy based on your domain.
- Source inventory
- Validated architecture
- Chunking strategy
Ingestion & indexing
Connection to sources (Notion API, Confluence, PDF parsers), intelligent chunking, embedding generation and indexing in the vector DB with structured metadata.
- Sources connected
- Embeddings indexed
- Vector DB operational
RAG pipeline & tests
RAG pipeline assembly (retrieval + generation), precision tests with RAGAS, system prompt optimization and validation on your real use cases.
- RAG pipeline live
- RAGAS score > 90%
- Tests validated
Monitoring & improvement
Real-time monitoring dashboard, automatic feedback loop, re-indexing when sources change and monthly RAG quality reports.
- Live dashboard
- Quality alerts
- Auto re-indexing
Everything about RAG knowledge base.
Tout ce que vous voulez savoir avant de déployer votre RAG.
RAG = Retrieval-Augmented Generation. The AI doesn't generate from its parameters - it first retrieves the exact passages from your documents that answer the question, then generates a response grounded in those sources. Result: 0 hallucination, sources always cited.
A classic chatbot invents plausibly. A RAG retrieves factually. Our system guarantees every answer comes from your official documents - with the exact source and relevance score displayed.
24h to index up to 10,000 documents. Initial indexing takes a few hours depending on volume, then new sources are ingested in real time via webhook automatically.
Notion, Confluence, SharePoint, Google Drive, Salesforce, PDF, Word, SQL databases, REST APIs - any source exposing structured or semi-structured data. We also connect your CRM and helpdesk tools.
Pinecone for large-scale SaaS projects, pgvector (PostgreSQL) for a 100% managed stack, Qdrant for on-premise needs. We adapt to your existing infrastructure.
We use the RAGAS framework: faithfulness (answer grounded in sources), answer relevancy (relevance), context precision (retrieval quality). Target score > 90% before production.
Zero hallucination. Sources always cited.
Free document audit, RAG architecture validated in 24h, deployment in 3 days. Your data, your AI, your total control.
Complementary expertise