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Your AI powered by your own documents

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AI Augmented by your documents

Imagine an assistant capable of responding precisely to all your questions, based directly on your own documents: manuals, procedures, guides, internal reports… Our Retrieval and Augmented Generation technology combines intelligent analysis of your content with the power of a generative AI response engine:
  • It reads and understands your documents to identify the most relevant information.
  • It generates clear and personalized responses, adapted to your business and your needs.
No more generic or approximate responses: your teams and your customers obtain reliable and relevant information, instantly. Whether for chatbots, virtual assistants, or document analysis tools, this solution transforms your data into an intelligent knowledge engine at your service.

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RAG process step by step

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1. Document ingestion

Collect and centralize all your documents: PDF, Word, Excel, emails, code source.
Prepare documents with metadata: author, date, category.
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2. Vectorization and indexing

Transform text into vector embeddings using OpenAI or other models.
Create a vector index for fast similarity search.
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3. Contextual search

When a question is asked, the engine searches the most relevant documents via similarity measures.
Return only the most relevant passages for the context.
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4. Augmented generation

Use the retrieved passages as context to generate a precise AI response.
Reduce generic or incorrect responses through contextualization.
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5. Feedback and continuous learning

Responses can be annotated or validated to improve the model and index.
Automatic index update and scoring adjustment.
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Key advantages

Guaranteed precision

Responses based exclusively on your real and verified data.

Maximum security

All your data remains on your servers, never externalized.

Automatic updates

Seamless integration of new documents without interruption.

Optimized performance

Instant search even in large volumes of data.

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Technologies used

  • Pinecone/Chroma: High-performance vector storage and search
  • OpenAI Embeddings: Advanced semantic vectorization
  • Automatic indexing: Real-time document updates

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Concrete examples

User question: “What is the procedure for validating international orders?”Analyzed documents: quality manual, customs procedures, international regulationsGenerated response:
“According to the Q-PROC-INT-001 procedure, international order validation involves:
  1. Verification of customs compliance (DOUAN-2024 document)
  2. Applicable incoterms validation
  3. Required export license control
Estimated duration: 2-3 working days.”
User question: “How does the internal recruitment process work?”Analyzed documents: HR guide, internal policies, evaluation formsGenerated response:
“The internal recruitment process includes:
  1. Submission of application via internal portal
  2. Interview with department manager
  3. HR department validation and job profile update
Estimated duration: 1-2 weeks.”
User question: “How to obtain a quarterly financial report?”Analyzed documents: accounting procedures, ERP guides, confidentiality policiesGenerated response:
“To obtain a quarterly financial report:
  1. Log in to the ERP portal with your credentials
  2. Select the period and report type
  3. Export the document in PDF or Excel format
Average delay: immediate after generation.”
User question: “How to resolve a network printer failure?”Analyzed documents: IT guide, maintenance procedures, previous ticketsGenerated response:
“Steps to resolve a network printer failure:
  1. Check network connection and power
  2. Restart the print server
  3. Consult error logs and apply documented fixes
Estimated resolution: 30-60 minutes depending on complexity.”

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For optimal performance, the RAG requires a structured and well-organized document base. We accompany the implementation of your document index.

Maximize efficiency by enriching your documents with metadata: author, date, category, keywords.