Impenetrable static documents, full of jargon and hidden single points of failure. Here is how SAFOPS is fixing the industry.
SAFOPS models your hazards and mitigations intuitively, allowing you to instantly identify when fault sequences punch through your safety barriers.
Safety cases are typically massive, 500-page static PDFs. The moment they are published, they are out of date. When equipment changes, engineers must manually hunt down every text reference across hundreds of pages.
Relational Data. Hazards and barriers are structured data entities. Change a barrier's reliability rating once, and it instantly updates across every fault sequence it protects.
Highly paid safety engineers spend 80% of their time writing boilerplate hazard descriptions and formatting tables rather than performing actual safety analysis. It costs time and money.
Local AI Assistants. Secure, air-gapped LLMs instantly draft hazard descriptions and propose barriers. It turns the engineer from a writer into an editor, cutting drafting time by up to 80%.
Safety cases are written by safety engineers for regulators, filled with dense jargon. Operations staff and management often cannot understand them, leading to a poor safety culture on the shop floor.
Visual Fault Sequence Diagrams. We translate complex fault sequences into clean, intuitive diagrams. Anyone can look at the map and immediately understand: what goes wrong, what stops it, and what happens if it fails.
When hazards are listed in flat Word tables, it is easy to accidentally rely on the same utility (e.g., electrical power) for five different independent barriers, creating a hidden single point of failure.
Interactive Fault Sequence Diagrams. Because the tool maps relationships visually, systemic weaknesses become obvious. If a barrier doesn't meet the target reliability dictated by the Risk Matrix, or has a recorded deficiency, the system flags it right on the diagram — not in a separate tracker you have to remember to check.
When regulators ask why a specific barrier was downgraded three years ago, the justification is often lost in an old email chain or a departed engineer's notebook.
Tamper-Evident Provenance. Every change—whether human or AI—is tracked and hash-chained (SHA-256, one entry linked to the next), so altering, deleting, or reordering any past entry is immediately detectable, not just logged. AI-generated text is tagged per-field until formally signed off.
Legacy safety software forces you to purchase disconnected "modules" (one for fault trees, one for reliability block diagrams) and mandates expensive "e-learning" just to navigate their complex interfaces.
A Unified, AI-Guided Platform. SAFOPS is a single, continuous workflow from initiating event to ALARP justification. No fragmented modules, and no week-long training courses required—the AI Copilot guides you natively.
Engineers and stakeholders get lost in thousands of disconnected rows and columns in excel spreadsheets and databases, making it impossible to see the big picture across the CADMID lifecycle.
Visual, Live Hazard Logs. SAFOPS isn't just a giant spreadsheet of hazards. It generates a Live Hazard Log dynamically from visual Fault Sequence Diagrams. You see exactly how risks connect, rather than just staring at a database table.