AUSTRIK AI
Contac details:
Company: BESTEIRO INGENIERÍA S.L.
Contact: José Luis Besteiro
Telephone: +34 664 476 377
Email: info@besting.es
Webside: www.besting.es
Project description:
AUTRISK AI is a digital platform designed to improve the identification, analysis, and management of operational incidents and risks in highly automated industrial environments, particularly within the automotive sector. In facilities equipped with industrial robots, automated production lines, material handling systems, and specialized machinery, many risk situations are not detected during the design or initial assessment phase but emerge during day-to-day operations.
The platform enables operators, maintenance technicians, and production managers to report anomalies, incidents, or potentially hazardous conditions directly from the shop floor, preventing valuable operational knowledge from being lost or remaining confined to informal communications. The solution transforms these observations into structured information that supports production, maintenance, engineering, and industrial safety teams.
Solution & technologies:
The solution combines a user-friendly interface for incident reporting via mobile devices, tablets, or computers, artificial intelligence models capable of interpreting the reported information, and standardized industrial safety criteria to classify and prioritize risks.
For each reported incident, the platform identifies the associated risk type, estimates a preliminary risk level, structures the information, and makes it available to technical teams and decision-makers. Its development is supported by Besteiro Ingeniería’s expertise in machinery safety, industrial regulations, CE marking, equipment compliance, and risk assessment within automotive manufacturing environments.
Innovation contributed:
AUTRISK AI brings together three elements that traditionally operate independently: direct incident reporting from the shop floor, artificial intelligence applied to operational analysis, and industrial safety engineering methodologies. Its key differentiator lies in transforming operators’ tacit knowledge into structured and prioritized risks, reducing subjectivity in risk assessment and accelerating decision-making processes.
In addition, the platform democratizes early risk detection by enabling any employee to contribute valuable information without requiring specialist safety expertise, while ensuring that assessments are based on consistent, standardized, and fully traceable criteria.
