ChatIR
ChatIR is the productized version of the methodology behind this garden.
The 30% accident reduction at Royal Ambulance was proof that structured incident investigation — done right, capturing systemic factors instead of just surface symptoms — moves real numbers. ChatIR is that methodology scaled into a platform.
What It Does
ChatIR is an AI-powered safety incident investigation platform for EMS. The core problem it solves: standard incident forms capture what happened, not why. They generate checkbox data that tells you nothing about the conditions, decisions, and system failures that made the incident possible.
ChatIR’s AI interview surfaces those factors through structured conversation — the same Socratic method behind The Debrief, applied to incident investigation. The result is richer data, better root cause analysis, and the ability to identify patterns across incidents over time.
The analytical framework uses SPECS — Scene, Process & Policy, Equipment & Load, Crew, System — layered on HFACS. Each factor is a data point. The pattern across factors is what tells the real story.
The Clinical Metaphor
Think of it the way a medic thinks about a patient.
The incident type — a backing collision, an intersection near-miss — is the chief complaint. It’s a symptom. You don’t treat the symptom; you do a differential. You gather vitals. You look at the full presentation.
A single data point (one SPECS factor) tells a story. But its meaning changes depending on what else presents alongside it — just like a blood pressure reading means something different at HR 60 than at HR 200. The chord, not the individual note, is where the diagnosis lives.
ChatIR gathers the vitals. The analysis reads the chord.
Learn More
→ chatir.io — the platform
→ christopherjharper.com — consulting, background, contact
The methodology in ChatIR is the same one behind this garden. If you want the full framework in one place before you go — the EMS Supervisor Field Guide is on Gumroad, free with email.