For Hire: Make scenarios built to survive failure — error handling, tested recovery, and you own everything

I build Make scenarios that keep running when things break — error handling on every critical route, documented handoff, and no lock-in.

Proof of the standard I build to:

My lead build ran 90 days in production, checked itself every 60 seconds, and survived three deliberate attempts to kill it — forced shutdowns, broken scripts, network drops. It detected each failure fast, recovered clean, and never needed a rebuild. Full write-up: GitHub - amear238/self-healing-ai-infrastructure: An AI monitoring agent that watches production infrastructure every 60 seconds, diagnoses its own failures, fixes low-risk issues in scope, and escalates the rest for human approval. 90 days in production. · GitHub

What that discipline looks like in Make:

  • Error handlers as a requirement, not an afterthought. Every critical route gets a defined failure path — retry, resume, or stop-and-alert — so a scenario never dies silently and gets discovered by your customer instead of you.
  • Failure visibility. When something breaks, you get told — before it becomes a client-facing incident.
  • Human-in-the-loop where it matters. For anything risky, the scenario proposes and a human approves. Nothing irreversible runs without a yes.
  • Tested changes before production, where the setup allows it. For agency or white-label work that can mean sandbox or staging accounts, duplicated scenarios, or test data before anything touches live operations.

White-label and agency work:

I work behind the scenes. I do not contact your end client unless you ask me to. Handoff includes documentation for the scenarios, connections, failure paths and operating assumptions, so your team — or another competent Make builder — can understand what was built and maintain it later.

The other thing I build against is lock-in. Most automation gets sold with a hidden maintenance contract attached: the builder is the only one who can fix it. I think that’s a design choice, not a requirement. I build inside your Make account — you own the scenarios, the connections, and the documentation, and any competent builder can service what I hand over. Ongoing support is available if you want it; it is never required to keep your system alive.

Before I built automation, I ran businesses — family restaurants, a coffee shop, a martial arts gym. I know exactly what it costs when a system the business depends on goes down during service. That’s the standard I build to: not “it worked in the demo,” but “it kept working after we tried to break it.”

Rate: opening rate is in the low $40s per hour (USD). Fixed-scope quotes available once the scope is actually fixed.

Getting in touch: reply to this topic or send me a message with the thing in your business — or your client’s business — that’s wasting time or breaking too often. Tell me what you’re trying to automate, what apps are involved, and whether it’s direct or white-label. I’ll tell you straight whether it should be automated — and whether I’m the right person to build it.

Hi Amear — I have a paid Make + Airtable audit that may fit your white-label work: an existing AI document-intake workflow with data-integrity and reliability issues, using redacted data only.

Are you available for a fixed 24-hour audit this week? If so, please message me with your fixed price and one relevant proof example. No work would begin before the buyer funds the audit.

Hi, yes, happy to put on my investigator hat for this one this week.

One thing before I lock in the 24-hour window: does that clock start when I get access to the workflow, or when the audit gets funded? I’d rather commit to something I can actually hit instead of something vague.

Scope is a full audit of the intake pipeline’s data-integrity and reliability gaps. You’ll get a written findings report with fix priorities. Remediation isn’t included, that would be a separate follow-on if you want it done after.

On proof: I haven’t rebuilt your specific pipeline. But the failure class is one I’ve chased down before, wrong output without a crash, processes going silently dead. That’s exactly what GitHub - amear238/self-healing-ai-infrastructure: An AI monitoring agent that watches production infrastructure every 60 seconds, diagnoses its own failures, fixes low-risk issues in scope, and escalates the rest for human approval. 90 days in production. · GitHub catches. It runs in production on n8n. [CONFIRM: your actual hands-on Make/Airtable level. My day-to-day tooling is n8n-first, so I want to be upfront about that before you commit.]

I’ll get you a fixed price once I’ve had a look at the workflow and can scope it properly. No work starts until the audit is funded.