AI automation repair.
Your n8n, Make, Zapier or custom AI automation worked in the demo, then stopped — or worse, kept running and started getting things wrong. Send it in: you get a written diagnosis and a fix.
What usually broke
It was tested on three examples
Real inputs are messier than the demo ones. The fix is a test set of real cases, run on every change.
The model changed under it
Hosted models get updated and prompts drift. Pin versions where possible and re-run the test set on a schedule.
Nothing checks the output
An LLM step writing straight into a CRM or an email ships every mistake. A validator in between sends bad output to a review queue instead.
The integration broke, not the AI
Expired tokens, renamed columns, rate limits. A heartbeat run with a known input catches these within the hour.
Nobody owns it
Credentials in a former contractor's account, no runbook, no logs. Every automation needs a named owner.
The longer version: Why your AI automation keeps breaking — and the five fixes →
How a repair works
Questions
Which tools do you fix?
n8n, Make and Zapier workflows, and custom automations in Python or TypeScript — including the LLM calls inside them (OpenAI, Claude, open models) and the integrations around them: CRMs, spreadsheets, email, WhatsApp and Twilio.
Do you need my passwords?
No. A workflow export, or a temporary account with only the access the fix needs, is enough. Your keys and accounts stay yours.
What does it cost?
You get a written diagnosis first, and a fixed price for the fix before any work on it starts.
What if it should be rebuilt instead?
The diagnosis says so, and why. A long no-code workflow with several LLM calls in a row is often cheaper to keep alive as a small amount of tested code.
Book a repair.
Say which tool it runs on, what it should do, and what it does instead.