Your AI workflow works when everything goes right.
But what happens when the LLM is wrong?
I’m offering a focused reliability audit for one existing n8n AI workflow.
I look specifically for failure modes such as:
hallucinated or malformed LLM output
missing data reaching downstream systems
weak validation before CRM / database / API writes
unsafe fallback behavior
duplicate executions
API failures and retry problems
missing error branches
hard-coded assumptions
edge cases that are not covered by the happy path
What you receive
For $79 fixed, I review one bounded workflow/path and deliver:
prioritized findings: Critical / High / Medium / Low
explanation of each reliability risk
recommended fixes
a practical verification/test checklist
No production credentials or customer data required.
A sanitized workflow export plus synthetic/sample inputs is enough for the audit.
Example of my approach
I recently built an n8n AI quoting workflow where the key challenge wasn’t generating a quote — it was preventing the AI from generating a believable but incorrect one.
The workflow separates LLM extraction from deterministic validation and pricing.
A valid catalog product proceeds to deterministic pricing.
An unknown product instead produces:
BLOCKED_MISSING_PRODUCT
rather than allowing the LLM to invent a product reference or price.
Public case study, sanitized workflow and test evidence:
GitHub:
Introductory offer
$79 fixed for the first 3 audits.
This is an audit, not a full workflow rebuild. If you want me to implement the recommended fixes afterward, we can scope that separately.
If you’re interested, reply here or DM me with:
- What the workflow does
- What you’re worried might fail
- A sanitized export or screenshot if available
I’ll tell you whether it fits the audit scope before any paid work starts.