Leveraging Lessons Learned in Technical Documentation
Lessons learned (LL) form the foundation of high-precision engineering. At Ametra, this knowledge is stored in massive volumes of technical documents, expert reports, and business rules accumulated over several decades.
The current challenge is no longer to produce data, but to ensure its immediate accessibility and actual utilization by engineers during the design phase or operational maintenance (OM).
The Challenge of Unstructured Data and Confidentiality
The technological challenge is twofold.
On one hand, conventional search tools cannot interpret structured data (data tables, charts, nomograms) contained in PDF archives. On the other hand, sovereignty requirements prohibit the use of public generative AI, which guarantees neither confidentiality nor source reliability. The risk of technical hallucination is an unacceptable barrier in our sectors of activity (aerospace, defense, nuclear, etc.).
A Sovereign AI Demonstrator with ECE and Evolia Stratégie
Under the leadership of an internal AI committee, Ametra has completed a strategic proof of concept (POC). This project was conducted in partnership with the ECE engineering school, and notably mobilized the expertise of two engineers, Xavier Demange and Jean-François de Greling, through their firm Evolia Stratégie.
The system is based on a rigorous technical architecture:
- Selection of language models (LLM): selection of models suited to technical data processing, deployable on private infrastructure.
- Advanced multimodal reading: capability to extract and interpret information within tables and graphical elements, even in legacy documents.
- RAG (Retrieval-Augmented Generation) methodology: the AI draws exclusively from a pool of business documents validated by Ametra.
- Systematic sourcing: each response is precisely sourced. If the data is not present in the repository, the AI does not respond, ensuring total reliability.
Secure Deployment to Enhance Project Reliability
Performance tests confirm the viability of this sovereign model. This collaboration enables Ametra to scale the server infrastructure required for deployment in 2026.
For our clients, this advancement ensures that the group’s historical expertise is instantly accessible to each project team, within a fully secure data environment.
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