TECHNOLOGY
How it works

  • Reduction of engineering costs:

    • Automation of time consuming and error prone tasks

    • Processing of technical documentation

    • Risk identification and classification

    • Generation of risk analysis reports

    Reduction of critical failures:

    • Ability of scanning existing company knowledge and apply it to new projects

    • Seamless scaling with project complexity

    • One-click updates when new project information is available

    Improved knowledge management:

    • Indefinite retention of organizational knowledge

    • Collaborative review of risk analysis results with key stakeholders


  • FMEA (Failure Modes and Effects Analysis)

    FMEA identifies potential failure modes within a system and assesses their impact to prioritize mitigation efforts.

    • Pros: Systematic, helps prioritize risks, improves reliability.

    • Cons: Time-consuming, may miss complex interactions.

    FMECA (Failure Modes, Effects, and Criticality Analysis):

    FMECA extends FMEA by adding a criticality analysis to quantify the severity and likelihood of failures.

    • Pros: Detailed prioritization, quantifies risk, enhances decision-making.

    • Cons: More complex and resource-intensive than FMEA, relies on accurate data.

    HAZOP (Hazard and Operability Study)

    HAZOP examines processes to identify hazards and operability issues through systematic deviation analysis.

    • Pros: Thorough, identifies operational issues, enhances safety.

    • Cons: Requires expert knowledge, labor-intensive, may be subjective.

    STPA (System-Theoretic Process Analysis)

    STPA uses a systems theory approach to identify unsafe interactions and control flaws in complex systems.

    • Pros: Addresses complex interactions, focuses on control systems, adaptable to various domains.

    • Cons: Requires deep understanding of systems theory, only qualitative.

    • Reasoning of AI models transparently exposed to the users and domain experts

    • Automated identification of high uncertainty areas for the risk assessment

    • Traceability of AI-supported decisions throughout the project evolution