IncidentScribe vs. ChatGPT for postmortems
Plenty of engineers do draft postmortems by pasting the incident channel into ChatGPT. When it’s allowed and the data is non-sensitive, it works fine. Two things make it the wrong tool for a load-bearing incident review at a regulated buyer: where the data goes, and whether you can trust what comes back.
1. Where the data goes
Pasting incident data into ChatGPT sends it to OpenAI’s cloud. For SREs at banks, healthcare orgs, defence contractors, and most post-2023 enterprises, that’s a standing policy violation — and public AI data-exposure incidents keep hardening those bans. IncidentScribe runs Apple’s Foundation Models on-device; the incident data never leaves the Mac, and the App Sandbox enforces it.
2. Whether you can verify it
ChatGPT gives you a one-shot summary. It reads plausibly, and that’s the problem — you can’t tell which lines it’s drawing each claim from, or whether it invented a participant or shifted a timestamp. You either trust the whole draft or re-read the raw channel yourself. IncidentScribe’s citation chain makes every claim two clicks from its source line, and fabricated events are dropped during validation before you ever see them. You verify the chain instead of trusting the prose.
3. Structure, not a blob
ChatGPT returns whatever shape the prompt nudged it toward. IncidentScribe builds a validated timeline first, then drafts five consistent sections — Summary, Timeline, Root Cause, Contributing Factors, Action Items — against that structure. The Timeline section is rendered deterministically from the events, not written as free prose.
When ChatGPT is still the right call
If your org permits cloud LLMs and the incident is low-stakes, a quick ChatGPT pass is faster to reach for. IncidentScribe’s Pro tier even lets you bring your own cloud key if you want its structured, cited pipeline on top of a frontier model. But the default — and the whole reason the product exists — is the on-device path for the people ChatGPT can’t serve.