AI-native incident investigation and root cause analysis platform now supports Australia-specific EHS requirements, local data residency needs, and enterprise security expectations
ATLANTA, GA & SYDNEY, AU / ACCESS Newswire / June 9, 2026 / Haven Safety AI, the AI-native platform for incident investigation and root cause analysis in high-risk operations, today announced the availability of its platform in Australia, marking the company’s first international expansion.
The launch brings Haven’s safety intelligence platform to Australian organisations with capabilities designed for the country’s distinct EHS, Work Health and Safety, data residency, and enterprise security requirements. Haven’s knowledge graph has been localised to support Australian safety terminology, regulatory context, jurisdictional considerations, incident investigation workflows, and root cause analysis practices, enabling teams to conduct AI-assisted investigations with local operating context from the start.
The Australian release also includes support for local data residency requirements and security controls designed for organisations managing sensitive operational, workforce, and safety information.
“Australia was the clear choice for Haven’s first international market,” said Joseph Hanna, Co-Founder and CEO of Haven Safety AI. “The country has a deep and serious safety culture, especially across high-risk industries, and a strong willingness to adopt innovation when it can improve how work is actually done. We built Haven’s Australian availability around that reality: local WHS context, local data residency expectations, and the governance safety leaders need to use AI responsibly in incident investigation and RCA.”
Haven helps organisations move beyond digitised forms and static investigation templates by combining structured workflows, AI-assisted analysis, and a continuously learning knowledge graph. The platform supports safety teams through evidence collection, event reconstruction, causal analysis, corrective action development, investigation quality review, and enterprise-level learning across incidents.
For Australian organisations, Haven’s localised platform is designed to help teams:
Conduct incident investigations and RCA with Australia-specific WHS and EHS context
Improve investigation consistency across sites, teams, and business units
Identify recurring causal patterns, failed controls, and emerging operational risks
Strengthen corrective action quality and accountability
Maintain governance, auditability, and security for sensitive safety data
Support local data residency expectations for Australian operations
The Australian launch comes as safety leaders increasingly evaluate AI as a core layer of the next generation of EHS technology.
“Our team evaluated Haven as part of a consulting engagement supporting a major Australian energy company in selecting and deploying AI-enabled investigation and learning capability. We were impressed,” said Cam Stevens, Founder and CEO of Pocketknife Group. “Haven has been thoughtful about how AI and humans work together across the investigation and learning workflow. Their focus on helping organisations learn from incidents consistently and at scale is exactly where AI in workplace health and safety needs to go. We’re excited to see Haven in the Australian market.”
Pocketknife Group is an Australia based consultancy that works with organisations navigating safety innovation, digital transformation, critical risk management, and the integration of AI and emerging technology into safety-critical systems.
“Haven’s expansion into Australia is about more than making the product available in a new geography,” Hanna added. “It is about building a platform that understands the local regulatory environment, respects local data requirements, and helps Australian safety teams turn every investigation into a stronger prevention system.”
Haven is available now for Australian enterprise customers across energy, utilities, infrastructure, construction, manufacturing, logistics, mining, and other high-risk sectors.
