Sheet 01 — Sheet 01 — Module Artificial Intelligence
AI for a precise purpose, never as a marketing gimmick.
Internal assistant, document search, chatbot, classification, OCR, reporting
Sheet 02 — Sheet 02 — Observations
Why most enterprise AI projects fail.
- A chatbot deployed without scope boundaries that hallucinates or invents answers.
- Hours lost searching for information hidden in poorly indexed internal documents.
- Paper or scanned documents that must be re-typed manually
- An AI tool chosen solely for image, disconnected from real operational needs
Sheet 03 — Sheet 03 — What is Included
Pragmatic, measurable AI modules.
Internal AI Assistant
Answers to recurring questions generated directly from your own documents
Document Search (RAG)
Internal documentation searchable in natural language, citing exact sources for full traceability.
Grounded Chatbot
A chatbot limited to a defined scope with explicit safeguards
OCR & Computer Vision AI
Automated data extraction from scanned documents, invoices, delivery notes, or photos.
Classification & Automated Sorting
Emails, requests, or documents automatically categorized and routed to the right folder or team member.
Summarization & Automated Report Generation
Automated synthesis of intervention reports or production metrics
Sheet 04 — Sheet 04 — Process & Workflow
The same rigorous engineering process as for every module.
Study, design, development, commissioning, training, and follow-up.
Explore full methodology in detail →Sheet 05 — Sheet 05 — Use Case
A concrete example.
A technical team repeatedly searches for the same procedure across hundreds of scattered PDF pages. With a search assistant, queries take seconds.
The assistant on this site is a lightweight demonstration of this capability!
Sheet 06 — Sheet 06 — Technical Stack
Tech stack tailored to project needs and data sensitivity.
Sheet 07 — Sheet 07 — FAQ
Frequently asked questions about AI solutions
Do our sensitive documents remain internal?
Depending on data sensitivity, local AI models (off-cloud) can be deployed.
How do we prevent AI from fabricating answers?
By strictly constraining its scope and grounding its answers in your documents.
Do we need massive datasets to start an AI project?
No — most use cases (RAG search, OCR, document routing) run effectively on reasonable document volumes.
Can we start with a small pilot scope?
This is the recommended approach: a precise, validated, and measured use case.
Sheet 08 — Sheet 08 — Next Steps
Let us discuss your specific AI use case
Describe the task you want to streamline — personal response within 48h.