Use case
Train the response after an incident — across every location, in days
Turn the post-incident SOP into AI-built training every relevant frontliner finishes within days. HQ holds the response trail per location, per learner.
The challenge
Something happened. A near-miss on the floor, a slip-and-fall, a kitchen burn, an aggressive customer interaction, a safety SOP that turned out to have a gap when tested for real. The first 48 hours, HQ tightens the procedure, legal weighs in, ops drafts the new guidance. Then comes the hard part: getting every relevant frontliner — at the location where it happened, at every comparable location across the network — actually trained on the new response, fast, with a record. The standard play is an email to managers, a staff-meeting note, and a hope that it sticks. Six months later, when the next incident or audit happens, you can't prove who was retrained, when, on what version of the SOP — and the same gap can recur at a different location.
The solution
Aristotl turns the post-incident SOP into AI-built retraining every relevant frontliner completes within days — gated by role, by location, by risk profile. Upload the revised procedure, AI builds a course covering the change, the scenario, and the right response — with scenario-based knowledge checks that test what the frontliner actually does on the floor, not just what they recognize on paper. Push to the location where it happened first, then to every comparable location across the network. HQ holds a defensible response trail: who was retrained, on what version of the SOP, when. When the next audit or incident comes, the trail is intact — and the gap that started this doesn't recur because the retraining was tracked, not assumed.
Key benefits
- Post-incident retraining live across the network in days
- Scenario-based checks — tests response, not just recognition
- Defensible trail per location, per learner, per SOP version
- Risk-targeted: scoped to roles and locations actually exposed
- Same gap doesn't recur — because retraining was tracked, not assumed
How it works
Upload the revised post-incident SOP
Drop in the tightened procedure as ops and legal locked it. Aristotl preserves the source version for the response record.
AI builds the scenario-based retraining
Aristotl generates a course covering the change, the scenario, and the right response. Scenario-based checks test floor-level decision making, not paper recall.
Push by risk profile — exposed roles first
Send first to the location where it happened, then to every comparable location across the network — scoped to the roles actually exposed to the risk.
Hold the defensible response trail
HQ sees who was retrained, on what version of the SOP, when. Audit-ready exports prove response — not as a hope but as a record, per learner, per location.