Now in early access — n8n, Make, Zapier
Your automation ran fine.
Was the output actually right?
FlowAudit watches the AI steps inside your no-code workflows and catches when they quietly produce wrong, malformed, or low-quality output — not just when the workflow crashes. Uptime monitors tell you it ran. FlowAudit tells you if you can trust what it did.
Get Early Access →
$ flowaudit check --workflow "lead-classifier" --run 8823
> workflow status: success (n8n reports: completed)
> checking AI step output against expected schema...
FAIL — 14 of 50 leads misclassified as "spam" (confidence < 0.3)
This is what your uptime monitor can't see.
Two different questions. Only one is being answered.
What uptime monitoring checks
"Did the workflow run?"
Heartbeats, dead-man's switches, error triggers. Good tools already do this well. It tells you the lights are on.
What FlowAudit checks
"Was what it produced actually correct?"
A workflow can report success, process every record, and still have the AI step hallucinate, misclassify, or return malformed data. Nobody's watching for that — until a customer notices.
How it works
Drop one node into your existing workflow. No migration, no rebuild.
01
Define what "right" looks like
A schema, a few sample-based rules, or a reference set of known-good outputs for that step. Takes minutes, not a data science project.
02
FlowAudit checks every run
Add the FlowAudit node after your AI step. It scores the actual output against your rules — format, confidence, drift from expected patterns.
03
Get alerted before your client does
Slack/email alert with the exact run, the exact bad output, and why it failed your check — not just "something happened."
Early access is limited
If you build or run n8n/Make/Zapier automations with AI steps in them, we want to talk to you before general availability. Leave your email and we'll reach out.