Centralise your data
related to non-conformities to immediately find similar cases
Used by leading industrial companies
Yxir allows us with AI to analyze our data, find similarities, and have a short lead time on the processing of non-conformities.
This allows us to identify what repeats from one project to another and possibly anticipate it. We reduce the costs of non-quality through this lead time, by reducing current non-conformities and anticipating them for future projects.
Quality Engineer
A treatment of non-conformitiesguided by your data
By centralizing access to existing data, Yxir uses AI to assist with every stage of non-conformity management: from initial reporting to the implementation of corrective actions, systematically drawing on historical data and similar cases.
1.Receive a complete statement from the start
Teams report non-conformities directly in Yxir, with AI assistance that ensures all necessary information is collected right from the creation of the file.
Simple and guided reporting interface
AI assistance for writing and structuring
Image and attachment analysis
Automatic verification of key elements (5W1H, 5 Whys)
2.Instantly find similar cases and useful resources
Based on the declared non-conformity, the AI analyzes your historical data to identify similar cases, the relevant context, and the information to consult to understand the non-conformity.
Centralized access to existing data (without forced migration)
Immediate presentation of similar non-conformities
Contextualization from the history, methods, and internal practices
Summary of key information to analyze
3.Identify the cause and the corrective actions to be taken
By relying on the analysis of similarities and history, Yxir helps to formulate probable causes and proposes reasoned corrective actions tailored to your context.
AI analysis of potential causes
Reasoned recommendations for corrective actions
Consideration of your methods, constraints, and teams
Assistance in formalising decisions
When everyone speaks their own language, no one can understand each other — or learn from one another
Three types of invisible barriers:
Language barriers
English, French, Chinese, Arabic — word-for-word translation is not enough. Technical understanding and knowledge transfer need to be conveyed, not just the words.
Generational barriers
Teams, equipment, and programmes evolve: over time, past learnings are lost because the names of machines, techniques, and problems have changed.
Structural barriers
Two different sites cannot learn from each other because the similarity is invisible to them: site-specific names and codes, distinct tools, processes named differently.
Language barriers
English, French, Chinese, Arabic — word-for-word translation is not enough. Technical understanding and knowledge transfer need to be conveyed, not just the words.
Generational barriers
Teams, equipment, and programmes evolve: over time, past learnings are lost because the names of machines, techniques, and problems have changed.
Structural barriers
Two different sites cannot learn from each other because the similarity is invisible to them: site-specific names and codes, distinct tools, processes named differently.
