AI-PSM is designed to combine your company’s operating history with purpose-built process-safety logic, governed RAGAGEP context and broader industry lessons — with a future permissioned PSE Hub that can extend learning across participating operators.
Request a Five-Year AssessmentEvents, maintenance, inspection, barrier information and corrective actions live in different systems, sites and local histories. Important lessons are often difficult to retrieve when the next event occurs.
RAGAGEP, public incident findings, technical guidance and operating experience evolve as the industry learns from accidents, failure mechanisms and improved understanding of hazards.
Use AI to accelerate retrieval, comparison and pattern finding while qualified people retain responsibility for engineering, process-safety, regulatory and operating decisions. Where clients opt in, a future PSE Hub can add anonymized and aggregated peer-learning patterns.
Repeat equipment failures, degraded barriers, process excursions, weak investigations and ineffective corrective actions rarely stay inside one organizational silo. AI-PSM is designed to connect these signals so teams can learn across disciplines and sites.
AI-PSM is designed to combine your operating history with a governed external knowledge layer so investigators and leaders can compare what is happening inside the plant with what standards bodies, regulators, public investigations, technical literature and the wider industry have already learned.
Connect incidents, near misses, RCFA findings, corrective actions, maintenance, inspection, assets and barriers across years, units and sites.
Bring appropriately licensed standards, applicable recommended practices, regulatory guidance, public incident investigations, technical literature and curated industry lessons into the same reasoning environment.
As the platform matures, participating clients can voluntarily authorize appropriately anonymized and aggregated PSE and near-miss patterns for governed peer comparison without exposing another operator’s proprietary event records.
Public investigations and RAGAGEP provide broad external context. Enterprise data shows how one company learns from itself. A voluntary, permissioned PSE Hub can add a third perspective: anonymized and aggregated peer patterns from participating operators.
OSHA’s PSM enforcement guidance addresses documented compliance with applicable RAGAGEP. EPA’s current RMP Program 3 guidance says owners and operators are expected to regularly review new and updated RAGAGEP and evaluate safety gaps created by new industry knowledge. AI-PSM is designed to help surface relevant knowledge for qualified review — not to independently determine or certify compliance.
OSHA RAGAGEP enforcement guidance ↗ · EPA RMP guidance on new and updated RAGAGEP ↗
Large operators can buy models, cloud platforms and developer tools. Reproducing AI-PSM means more than calling an LLM: the organization must also normalize years of operating data, encode domain logic, curate external knowledge, maintain retrieval and evaluation pipelines, govern source rights, monitor model quality and keep the system current as practices and technology change. It also cannot independently recreate a permissioned cross-company learning network.
Assemble the product team, domain model, taxonomies, historical normalization, RAGAGEP knowledge base, public-industry sources, retrieval architecture, evaluations, security, governance and ongoing maintenance.
Bring your data and SME judgment into a maintained framework designed for PSE learning, RCFA, asset/barrier context, cross-site reasoning, external knowledge retrieval and auditable review.
Use internal engineers to evaluate and act on risk — not to recreate every component of a continuously maintained industrial AI product and external knowledge-curation program.
It is not positioned as autonomous plant control, automatic regulatory reporting, or a substitute for competent engineering and process-safety judgment. Safety-critical conclusions and operating decisions remain subject to qualified human review and approval.
A five-year PSE assessment lets plant and enterprise leaders judge whether AI-PSM finds meaningful additional lessons before making a broader SaaS commitment.