SoterAI: A Proven Safety‑Critical AI System
Why Relying on LLMs Alone for Safety-Critical Tasks Introduces Unnecessary Risk
The first independent benchmark designed for real-world safety and risk management tasks shows SoterAI achieves 95.64% overall — outperforming every general-purpose LLM tested, including GPT, Claude, Gemini, and Grok.
Executive summary
General-purpose AI models — ChatGPT, Claude, Grok, Gemini — excel at language tasks. But they are fundamentally unsuited for domains where omissions, ambiguity, and non-deterministic conclusions carry real-world consequences.
SAFE-Bench (safe-bench.com) is the first independent benchmark designed specifically to evaluate AI performance on real-world safety and risk management tasks. Evaluated by a board of certified safety professionals using blinded protocols, the results are unambiguous.
95.64%
SoterAI overall score — 8+ points above the next best LLM
100%
Perfect score in Safety Intelligence and Incident Investigation
60 pts
Gap in Hazard Identification between SoterAI (95%) and worst LLM (35%)
8 models
Tested: GPT-5.1, GPT-4o, Claude Sonnet 4.5, Gemini 2.5 Pro, Grok4, and more
About SAFE-Bench
Traditional AI benchmarks measure language fluency, reasoning, and general problem-solving. They do not measure what matters most in safety work: the ability to identify all hazards, provide deterministic conclusions, and deliver auditable reasoning chains.
SAFE-Bench evaluates AI systems across seven critical domains, administered by an independent board of certified safety professionals — CSPs, loss prevention directors, HSE managers, and OSHA inspectors — using blinded protocols. No model knew it was being scored.
All evaluations were conducted under a blinded review process. Board members did not know which AI model produced each output — scoring was based strictly on accuracy, completeness, and production-readiness.
Seven domains of testing
Safety Intelligence
100 multiple-choice questions derived from CCSP-aligned materials covering OSHA regulations, hazard controls, incidence rate calculations, and emergency management.
Hazard Identification
Visual analysis of industrial, construction, warehouse, and maintenance images. Models must detect all meaningful hazards — explicit, implicit, and secondary.
Ergonomics
Video-based assessment of human motion, posture, force, and repetition. Models must classify severity using REBA or RULA frameworks and recommend targeted controls.
Policy Review
Analysis of written safety programs and procedures to identify deficiencies, missing elements, and non-compliant language relative to OSHA and international standards.
Incident Investigation
Structured root cause analysis using 5 Whys, TapRooT, and Fishbone methodologies from narrative descriptions, witness statements, and evidence packets.
Generative Materials
Production of toolbox talks, safety bulletins, and training materials accurate enough for frontline deployment.
RAMS (Risk Assessment)
Full risk assessment and method statement documents — hazard identification, risk ratings, hierarchy-of-controls proposals, and audit-ready output.
Benchmark results
SoterAI's 95.64% overall score represents an 8.14 percentage point advantage over the next-best model. The spread between best (SoterAI) and worst (Microsoft Copilot at 77.07%) is 18.57 points.
| Domain | SoterAI | Best LLM | Worst LLM |
|---|---|---|---|
| Safety Intelligence | 100% | 98% | 89% |
| Hazard Identification | 95% | 90% | 35% |
| Policy Review | 95% | 95% | 85% |
| Ergonomics | 95% | 92% | 77.5% |
| Incident Investigation | 100% | 90% | 60% |
| Generative Materials | 95% | 93% | 72% |
| RAMS | 88% | 85% | 76% |
| Overall | 95.64% | 87.50% | 77.07% |
The hazard identification gap
Hazard Identification shows the largest performance spread of any domain: 60 percentage points. Microsoft Copilot's 35% score means it misses nearly two-thirds of workplace hazards. In a facility with 100 potential hazards, that translates to 65 missed hazards — some minor, others potentially catastrophic.
Why the gap is so large
Practical implications
The data reveals a clear conclusion: relying on LLMs alone for safety-critical tasks introduces unnecessary operational and liability risk. Even the highest-performing AI systems require mandatory verification by qualified safety professionals before deployment.
SoterAI's specialized sub-agentic architecture — built from the ground up for safety-critical applications — consistently outperforms general-purpose models across every domain evaluated. The white paper presents the full methodology, domain breakdowns, and practical guidance for safety teams evaluating AI tools.
Download the full white paper
Get the complete 20-page report — full methodology, domain-by-domain breakdowns, and practical implications for safety teams evaluating AI tools.