AI Safety Risk Management Suite
Budget: $250 – $750 AUD
I want to put an end-to-end AI-driven safety risk management suite into production. The core of the platform must be a robust risk register and management module, because every other function—automated risk assessments, mitigation planning, tracking and reporting—will feed into or read from that single source of truth.
Scope of work
The build covers the entire life-cycle of a safety system: interested-party intake, regulations & standards registers, hazard registers, hazard identification, the live risk register, audit workflows, incident investigation support, change management, manual/document control, report generation and a real-time dashboard. Safety officers, regulatory auditors and incident investigators will use the application every day, so the interface needs to present tailored views and permissions for each role.
What I expect you to deliver
• Architecture and deployment of the AI agent layer (e.g., Python, LangChain or similar) that orchestrates GPT-based reasoning with structured data in a vector/relational store.
• A risk register module that lets users create, prioritise, update and close risks while the agent performs automated assessments, suggests mitigation actions and logs residual risk.
• Workflow automations for mitigation planning and audit evidence collection, with transparent version control for compliance.
• Dashboards and canned reports (Power BI, Grafana or comparable) that let us track KPIs, overdue actions and audit readiness at a glance.
• Documentation: user manual, admin guide and a short technical runbook that lets my in-house IT team maintain the system.
Acceptance criteria
• The risk register handles at least 2,000 concurrent records with sub-second query time.
• Automated assessments must calculate severity × likelihood and assign a risk level that matches ISO 9001 methodology.
• Mitigation suggestions are explainable: every recommended control cites the regulation, standard or past incident that triggered it.
• Role-based dashboards surface only the data relevant to Safety officers, Regulatory auditors or Incident investigators.
• All code, prompts and configuration files are handed over in a private repository and pass a hand-over walkthrough.
If you have demonstrable experience integrating AI agents with compliance or safety workflows, tell me how you would approach the data model and which tools you would choose. I’m ready to start as soon as we agree on the implementation plan and milestones.
Scope of work
The build covers the entire life-cycle of a safety system: interested-party intake, regulations & standards registers, hazard registers, hazard identification, the live risk register, audit workflows, incident investigation support, change management, manual/document control, report generation and a real-time dashboard. Safety officers, regulatory auditors and incident investigators will use the application every day, so the interface needs to present tailored views and permissions for each role.
What I expect you to deliver
• Architecture and deployment of the AI agent layer (e.g., Python, LangChain or similar) that orchestrates GPT-based reasoning with structured data in a vector/relational store.
• A risk register module that lets users create, prioritise, update and close risks while the agent performs automated assessments, suggests mitigation actions and logs residual risk.
• Workflow automations for mitigation planning and audit evidence collection, with transparent version control for compliance.
• Dashboards and canned reports (Power BI, Grafana or comparable) that let us track KPIs, overdue actions and audit readiness at a glance.
• Documentation: user manual, admin guide and a short technical runbook that lets my in-house IT team maintain the system.
Acceptance criteria
• The risk register handles at least 2,000 concurrent records with sub-second query time.
• Automated assessments must calculate severity × likelihood and assign a risk level that matches ISO 9001 methodology.
• Mitigation suggestions are explainable: every recommended control cites the regulation, standard or past incident that triggered it.
• Role-based dashboards surface only the data relevant to Safety officers, Regulatory auditors or Incident investigators.
• All code, prompts and configuration files are handed over in a private repository and pass a hand-over walkthrough.
If you have demonstrable experience integrating AI agents with compliance or safety workflows, tell me how you would approach the data model and which tools you would choose. I’m ready to start as soon as we agree on the implementation plan and milestones.
Related categories:
PHP
Python
Software Architecture
Risk Management
Database Programming
LangChain
AI Development
AI Agents