The automation may save time until it fails quietly, duplicates records, loops on bad data, or leaves nobody sure which system is correct. The business often notices only after an operator has been reconciling the mess manually for weeks.
Inventory of triggers, jobs, owners, and failure modes.
Map of no-code steps, scripts, scheduled jobs, webhooks, and manual workarounds.
Risk ranking based on customer impact, financial impact, and frequency.
/ stabilization
Keep the useful behavior.
Cleanup does not always mean rebuild. We preserve the workflow value while making the foundation easier to inspect, test, and support.
Monitoring for stale or failed automations.
Clear source-of-truth decisions.
Documentation for the team that owns the process.
/ engineering path
Move only the risky parts into stronger software.
Some automations can stay in Zapier, Make, or a spreadsheet. Others should become typed, observable services with queues, retries, idempotency, and tests because the cost of failure is too high.
Replacement plan for fragile steps that run business-critical work.
Retry, deduplication, and manual recovery for partial failures.
Automated tests around the rules that used to live in someone’s memory.
/ ownership
Give the workflow a visible owner.
The real goal is not cleaner diagrams. It is an automation system the business can own: documented triggers, expected behavior, support alerts, and a clear path for changes.
Runbook for common failures and recovery decisions.
Dashboards or alerts for failed jobs, stale records, and unusual volume.
Change log so future updates do not break invisible dependencies.
Yes. We can audit them, improve them, or replace the riskiest pieces with custom software where reliability, observability, and ownership matter.
How do we know what to rebuild first?
We prioritize automations by business impact, failure frequency, data sensitivity, and how hard they are to recover manually. The first rebuild is usually the one where a quiet failure costs the most.